{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1378d2bf",
   "metadata": {},
   "source": [
    "# Transfer Learning for Time Series Forecasting with Darts\n",
    "Authors: Julien Herzen, Florian Ravasi, Guillaume Raille, Gaël Grosch.\n",
    "\n",
    "## Overview\n",
    "The goal of this notebook is to explore transfer learning for time series forecasting -- that is, training forecasting models on one time series dataset and using it on another. The notebook is 100% self-contained -- i.e., it also contains the necessary commands to install dependencies and download the datasets being used.\n",
    "\n",
    "Depending on what constitutes a \"learning task\", what we call transfer learning here can also be seen under the angle of meta-learning (or \"learning to learn\"), where models can adapt themselves to new tasks (e.g. forecasting a new time series) at inference time without further training [1].\n",
    "\n",
    "This notebook is an adaptation of a workshop on \"Forecasting and Meta-Learning\" that was given at the Applied Machine Learning Days conference in Lausanne, Switzerland, in March 2022.\n",
    "It contains the following parts:\n",
    "\n",
    "* **Part 0:** Initial setup - imports, functions to download data, etc.\n",
    "* **Part 1:** Forecasting passenger counts series for 300 airlines (`air` dataset). We will train one model per series.\n",
    "* **Part 2:** Using \"global\" models - i.e., models trained on all 300 series simultaneously.\n",
    "* **Part 3:** We will try some transfer learning, and see what happens if we train some global models on one (big) dataset (`m4` dataset) and use them on another dataset.\n",
    "* **Part 4:** We will reuse our pre-trained model(s) of Part 3 on another new dataset (`m3` dataset) and see how it compares to models specifically trained on this dataset.\n",
    "\n",
    "The compute durations written for the different models have been obtained by running the notebook on a Apple Silicon M2 CPU, with Python 3.10.13 and Darts 0.27.0.\n",
    "\n",
    "## Part 0: Setup\n",
    "First, we need to have the right libraries and make the right imports. For the deep learning models, it helps to have a GPU, but this is not mandatory.\n",
    "\n",
    "The following two cells need to be run only once. They install the dependencies and download all the required datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb27b941602401d91542211134fc71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "pip install xlrd\n",
    "pip install darts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ca559b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 1716k  100 1716k    0     0  6899k      0 --:--:-- --:--:-- --:--:-- 6949k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 56.3M    0 56.3M    0     0  1747k      0 --:--:--  0:00:33 --:--:-- 2142k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 87.4M  100 87.4M    0     0  26.2M      0  0:00:03  0:00:03 --:--:-- 26.3M\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 4233k  100 4233k    0     0  5150k      0 --:--:-- --:--:-- --:--:-- 5163k      0      0 --:--:-- --:--:-- --:--:--     0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# Execute this cell once to download all three datasets\n",
    "curl -L https://forecasters.org/data/m3comp/M3C.xls -o m3_dataset.xls\n",
    "curl -L https://data.transportation.gov/api/views/xgub-n9bw/rows.csv -o carrier_passengers.csv\n",
    "curl -L https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/Train/Monthly-train.csv \\\n",
    "    -o m4_monthly.csv\n",
    "curl -L https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/M4-info.csv -o m4_metadata.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a027958",
   "metadata": {},
   "source": [
    "And now we import everything. Don't be afraid, we will uncover what these imports mean through the notebook :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9689b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "import random\n",
    "import time\n",
    "from datetime import datetime\n",
    "from itertools import product\n",
    "from typing import Optional\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.metrics import smape\n",
    "from darts.models import (\n",
    "    ARIMA,\n",
    "    ExponentialSmoothing,\n",
    "    KalmanForecaster,\n",
    "    LightGBMModel,\n",
    "    LinearRegressionModel,\n",
    "    NaiveSeasonal,\n",
    "    NBEATSModel,\n",
    "    RandomForestModel,\n",
    "    Theta,\n",
    ")\n",
    "from darts.utils.losses import SmapeLoss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5756350",
   "metadata": {},
   "source": [
    "We define the forecast horizon here - for all of the (monthly) time series used in this notebook, we'll be interested in forecasting 18 months in advance. We pick 18 months as this is what is used in the M3/M4 competitions for monthly series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "826351e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "HORIZON = 18"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab36bc6",
   "metadata": {},
   "source": [
    "### Datasets loading methods\n",
    "Here, we define some helper methods to load the three datasets we'll be playing with: `air`, `m3` and `m4`. \n",
    "\n",
    "All the methods below return two list of `TimeSeries`: one list of training series and one list of \"test\" series (of length `HORIZON`).\n",
    "\n",
    "For convenience, all the series are already scaled here, by multiplying each of them by a constant so that the largest value is 1. Such scaling is necessary for many models to work correctly (esp. deep learning models). It does not affect the sMAPE values, so we can evaluate the accuracy of our algorithms on the scaled series. In a real application, we would have to keep the Darts `Scaler` objects somewhere in order to inverse-scale the forecasts.\n",
    "\n",
    "If you are interested in seeing an example of how creating and scaling `TimeSeries` is done, you can inspect the function `load_m3()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff0a526b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_m3() -> tuple[list[TimeSeries], list[TimeSeries]]:\n",
    "    print(\"building M3 TimeSeries...\")\n",
    "\n",
    "    # Read DataFrame\n",
    "    df_m3 = pd.read_excel(\"m3_dataset.xls\", \"M3Month\")\n",
    "\n",
    "    # Build TimeSeries\n",
    "    m3_series = []\n",
    "    for row in tqdm(df_m3.iterrows()):\n",
    "        s = row[1]\n",
    "        start_year = int(s[\"Starting Year\"])\n",
    "        start_month = int(s[\"Starting Month\"])\n",
    "        values_series = s[6:].dropna()\n",
    "        if start_month == 0:\n",
    "            continue\n",
    "\n",
    "        start_date = datetime(year=start_year, month=start_month, day=1)\n",
    "        time_axis = pd.date_range(start_date, periods=len(values_series), freq=\"M\")\n",
    "        series = TimeSeries.from_times_and_values(\n",
    "            time_axis, values_series.values\n",
    "        ).astype(np.float32)\n",
    "        m3_series.append(series)\n",
    "\n",
    "    print(f\"\\nThere are {len(m3_series)} monthly series in the M3 dataset\")\n",
    "\n",
    "    # Split train/test\n",
    "    print(\"splitting train/test...\")\n",
    "    m3_train = [s[:-HORIZON] for s in m3_series]\n",
    "    m3_test = [s[-HORIZON:] for s in m3_series]\n",
    "\n",
    "    # Scale so that the largest value is 1\n",
    "    print(\"scaling...\")\n",
    "    scaler_m3 = Scaler(scaler=MaxAbsScaler())\n",
    "    m3_train_scaled: list[TimeSeries] = scaler_m3.fit_transform(m3_train)\n",
    "    m3_test_scaled: list[TimeSeries] = scaler_m3.transform(m3_test)\n",
    "\n",
    "    print(\n",
    "        f\"done. There are {len(m3_train_scaled)} series, with average training length {np.mean([len(s) for s in m3_train_scaled])}\"  # noqa: E501\n",
    "    )\n",
    "    return m3_train_scaled, m3_test_scaled\n",
    "\n",
    "\n",
    "def load_air() -> tuple[list[TimeSeries], list[TimeSeries]]:\n",
    "    # download csv file\n",
    "    df = pd.read_csv(\"carrier_passengers.csv\")\n",
    "    # extract relevant columns\n",
    "    df = df[[\"data_dte\", \"carrier\", \"Total\"]]\n",
    "    # aggregate per carrier and date\n",
    "    df = pd.DataFrame(df.groupby([\"carrier\", \"data_dte\"]).sum())\n",
    "    # move indexes to columns\n",
    "    df = df.reset_index()\n",
    "\n",
    "    # group bt carrier, specify time index and target variable\n",
    "    all_air_series = TimeSeries.from_group_dataframe(\n",
    "        df, group_cols=\"carrier\", time_col=\"data_dte\", value_cols=\"Total\", freq=\"MS\"\n",
    "    )\n",
    "\n",
    "    # Split train/test\n",
    "    print(\"splitting train/test...\")\n",
    "    air_train = []\n",
    "    air_test = []\n",
    "    for series in all_air_series:\n",
    "        # remove the end of the series\n",
    "        series = series[: pd.Timestamp(\"2019-12-31\")]\n",
    "        # convert to proper type\n",
    "        series = series.astype(np.float32)\n",
    "        # extract longest contiguous slice\n",
    "        try:\n",
    "            series = series.longest_contiguous_slice()\n",
    "        except Exception:\n",
    "            continue\n",
    "        # remove static covariates\n",
    "        series = series.with_static_covariates(None)\n",
    "        # remove short series\n",
    "        if len(series) >= 36 + HORIZON:\n",
    "            air_train.append(series[:-HORIZON])\n",
    "            air_test.append(series[-HORIZON:])\n",
    "\n",
    "    # Scale so that the largest value is 1\n",
    "    print(\"scaling series...\")\n",
    "    scaler_air = Scaler(scaler=MaxAbsScaler())\n",
    "    air_train_scaled: list[TimeSeries] = scaler_air.fit_transform(air_train)\n",
    "    air_test_scaled: list[TimeSeries] = scaler_air.transform(air_test)\n",
    "\n",
    "    print(\n",
    "        f\"done. There are {len(air_train_scaled)} series, with average training length {np.mean([len(s) for s in air_train_scaled])}\"  # noqa: E501\n",
    "    )\n",
    "    return air_train_scaled, air_test_scaled\n",
    "\n",
    "\n",
    "def load_m4(\n",
    "    max_number_series: Optional[int] = None,\n",
    ") -> tuple[list[TimeSeries], list[TimeSeries]]:\n",
    "    \"\"\"\n",
    "    Due to the size of the dataset, this function takes approximately 10 minutes.\n",
    "\n",
    "    Use the `max_number_series` parameter to reduce the computation time if necessary\n",
    "    \"\"\"\n",
    "    # Read data dataFrame\n",
    "    df_m4 = pd.read_csv(\"m4_monthly.csv\")\n",
    "    if max_number_series is not None:\n",
    "        df_m4 = df_m4[:max_number_series]\n",
    "    # Read metadata dataframe\n",
    "    df_meta = pd.read_csv(\"m4_metadata.csv\")\n",
    "    df_meta = df_meta.loc[df_meta.SP == \"Monthly\"]\n",
    "\n",
    "    # Build TimeSeries\n",
    "    m4_train = []\n",
    "    m4_test = []\n",
    "    for row in tqdm(df_m4.iterrows(), total=len(df_m4)):\n",
    "        s = row[1]\n",
    "        values_series = s[1:].dropna()\n",
    "        start_date = pd.Timestamp(\n",
    "            df_meta.loc[df_meta[\"M4id\"] == \"M1\", \"StartingDate\"].values[0]\n",
    "        )\n",
    "        time_axis = pd.date_range(start_date, periods=len(values_series), freq=\"M\")\n",
    "        series = TimeSeries.from_times_and_values(\n",
    "            time_axis, values_series.values\n",
    "        ).astype(np.float32)\n",
    "        # remove series with less than 48 training samples\n",
    "        if len(series) > 48 + HORIZON:\n",
    "            # Split train/test\n",
    "            m4_train.append(series[:-HORIZON])\n",
    "            m4_test.append(series[-HORIZON:])\n",
    "\n",
    "    print(f\"\\nThere are {len(m4_train)} monthly series in the M3 dataset\")\n",
    "\n",
    "    # Scale so that the largest value is 1\n",
    "    print(\"scaling...\")\n",
    "    scaler_m4 = Scaler(scaler=MaxAbsScaler())\n",
    "    m4_train_scaled: list[TimeSeries] = scaler_m4.fit_transform(m4_train)\n",
    "    m4_test_scaled: list[TimeSeries] = scaler_m4.transform(m4_test)\n",
    "\n",
    "    print(\n",
    "        f\"done. There are {len(m4_train_scaled)} series, with average training length {np.mean([len(s) for s in m4_train_scaled])}\"  # noqa: E501\n",
    "    )\n",
    "    return m4_train_scaled, m4_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fe37fa",
   "metadata": {},
   "source": [
    "Finally, we define a handy function to tell us how good a bunch of forecasted series are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a69b6b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_forecasts(\n",
    "    pred_series: list[TimeSeries], test_series: list[TimeSeries]\n",
    ") -> list[float]:\n",
    "    print(\"computing sMAPEs...\")\n",
    "    smapes = smape(test_series, pred_series)\n",
    "    plt.figure()\n",
    "    plt.hist(smapes, bins=50)\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.xlabel(\"sMAPE\")\n",
    "    plt.title(f\"Median sMAPE: {np.median(smapes):.3f}\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    return smapes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a59b4f",
   "metadata": {},
   "source": [
    "## Part 1: Local models on the `air` dataset\n",
    "\n",
    "### Inspecting Data\n",
    "\n",
    "The `air` dataset contains the number of air passengers that flew in or out of the USA per carrier (or airline company) from the year 2000 until 2019.\n",
    "\n",
    "First, we can load the train and test series by calling `load_air()` function that we have defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58890128",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IndexError: The type of your index was not matched.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "splitting train/test...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n",
      "IndexError: The type of your index was not matched.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaling series...\n",
      "done. There are 245 series, with average training length 154.06938775510204\n"
     ]
    }
   ],
   "source": [
    "air_train, air_test = load_air()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db486bff",
   "metadata": {},
   "source": [
    "It's a good idea to start by visualising a few of the series to get a sense of what they look like. We can plot a series by calling `series.plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "144c3c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax = plt.subplots(3, 2, figsize=(10, 10), dpi=100)\n",
    "\n",
    "for i, idx in enumerate([1, 20, 50, 100, 150, 200]):\n",
    "    axis = ax[i % 3, i % 2]\n",
    "    air_train[idx].plot(ax=axis)\n",
    "    axis.legend(air_train[idx].components)\n",
    "    axis.set_title(\"\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74829d15",
   "metadata": {},
   "source": [
    "We can see that most series look quite different, and they even have different time axes! For example some series start in Jan 2001 and others in April 2010.\n",
    "\n",
    "Let's see what is the shortest train series available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4564171e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min([len(s) for s in air_train])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db3ddac",
   "metadata": {},
   "source": [
    "### A useful function to evaluate models\n",
    "\n",
    "Below, we write a small function that will make our life easier for quickly trying and comparing different local models. We loop through each serie, fit a model and then evaluate on our test dataset. \n",
    "\n",
    "> ⚠️  `tqdm` is optional and is only there to help display the training progress (as you will see it can take some time when training 300+ time series)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e247f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_local_model(\n",
    "    train_series: list[TimeSeries], test_series: list[TimeSeries], model_cls, **kwargs\n",
    ") -> tuple[list[float], float]:\n",
    "    preds = []\n",
    "    start_time = time.time()\n",
    "    for series in tqdm(train_series):\n",
    "        model = model_cls(**kwargs)\n",
    "        model.fit(series)\n",
    "        pred = model.predict(n=HORIZON)\n",
    "        preds.append(pred)\n",
    "    elapsed_time = time.time() - start_time\n",
    "\n",
    "    smapes = eval_forecasts(preds, test_series)\n",
    "    return smapes, elapsed_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a176ed58",
   "metadata": {},
   "source": [
    "### Building and evaluating models\n",
    "\n",
    "We can now try a first forecasting model on this dataset. As a first step, it is usually a good practice to see how a (very) naive model blindly repeating the last value of the training series performs. This can be done in Darts using a [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82585d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e29327de9ef8492e8f52377388c6c13a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive1_smapes, naive1_time = eval_local_model(air_train, air_test, NaiveSeasonal, K=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed7039d",
   "metadata": {},
   "source": [
    "So the most naive model gives us a median sMAPE of about 27.2.\n",
    "\n",
    "Can we do better with a \"less naive\" model exploiting the fact that most monthly series have a seasonality of 12?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32ad3737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "746368d408d0424d8c3d9fec67f2b755",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive12_smapes, naive12_time = eval_local_model(\n",
    "    air_train, air_test, NaiveSeasonal, K=12\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdafd472",
   "metadata": {},
   "source": [
    "This is better. Let's try ExponentialSmoothing (by default, for monthly series, it will use a seasonality of 12):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6afdf37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a2234f887714fc6bd7495f74db5d35d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ets_smapes, ets_time = eval_local_model(air_train, air_test, ExponentialSmoothing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a33bfc",
   "metadata": {},
   "source": [
    "The median is slightly better than the naive seasonal model. Another model that we can quickly is the `Theta` method which has won the M3 competition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e38a16c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68339b0193764927af356fc3aa344e0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_smapes, theta_time = eval_local_model(air_train, air_test, Theta, theta=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b866091a",
   "metadata": {},
   "source": [
    "And how about ARIMA?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "033ab873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d8671fd2b774b15b5b973742133f2a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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U1FQ1aNDA6Y/9hQYOHKg//vGP+uCDDzRx4sQ6175z507HYa8LderUyREYVqxYoTFjxmjhwoXVnlR87tw5xcbGKicnR08++WSF96q5cJ8hISF6/vnnNX78eA0ZMkSxsbHavHmzFi9erBdffFHNmjVz3O6tt95SSkqKNm7c6Agd//M//6NVq1ZpwIAB+uGHHyo8hvL3srn55pt18803O20rPzzVtWtXp+Dz29/+VgsXLtSAAQP0u9/9Th06dNDnn3+uJUuW6L777lPPnj2rbiTgYwg3gJfMmTNHPXr00Ny5c/WHP/xB/v7+ioyM1MiRI3XXXXc55r3wwgsKDg7WnDlztHHjRvXs2VP/+Mc/qvyDXy4gIEBpaWl6/PHH9dJLLyk4OFiDBw/Wb3/72xpDRV1ER0dr+/bt+uCDD3T69GmFh4frtttuU2pqqmNVwBUlJSVatmyZ7rzzTqc/9hfq1q2boqKitHjxYpfCzZIlS5zem6jc6NGjq1wNqUp2drYyMzMlqdJXF128z9/85jcKCAjQa6+9plWrVikiIkKvv/66nnjiiRrv65tvvpEkpaWlKS0trcL2qt6orzpXX321vv76az3zzDNavHixTp06pXbt2mnSpElKSUmp8/4Ab7JZF65ZAwAA1HO8zw0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFG8+j43xcXFeumll7R9+3bl5eUpKipKEydO1A033KC0tDS98MILTlfOXbZsmdq0aePFigEAgK/z6spNWVmZ2rVrp/nz52vjxo0aNmyYJkyYoIKCAklSjx49tHnzZsfH5Qg2drtdR44cqXBRQdSM3rmH/rmO3rmH/rmH/rnuUvXOq+EmJCREY8eOVZs2beTn56fY2FgFBATo6NGj3iwLAADUYz51+YWMjAydPXtWEREROnjwoPbs2aN7771XzZo109ChQyu9cJ700+Gt4uJipzF/f3+nQ1q1VZ4eSeB1R+/cQ/9cR+/cQ//cQ/9cV1nv/PzcX3fxmcsvFBYWKjk5WXfddZfGjRun48ePy2azqU2bNvr22281adIkPfnkk7r33nsr3Hbu3LmaN2+e09iQIUOUkJBwucoHAAAe4M416cr5RLgpLS3VpEmTFBYWpueff142m63CnEWLFungwYN64YUXKmzz9MpNZmamIiIiPJIef07onXvon+vonXvon3von+sq650neuj1w1J2u11TpkyRzWbTc889V2mwkSSbzaaqclhgYKBLQaY6fn5+PEldRO/cQ/9cR+/cQ//cQ/9c5+neef27MH36dGVnZ2vGjBny9/9v1vrnP/+pnJwcSdJ3332npUuXqlevXt4qEwAA1BNeXbk5efKkVq5cqaCgIPXt29cx/uabb+rLL7/U1KlTdf78ebVq1UqjRo1SbGysF6sFAAD1gVfDTdu2bbVjx45Kt3Xv3l0TJky4zBUBAID6zuuHpQAAADyJcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGMXrF86E+6q62OiFfODi7wAAXBas3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAo/t4u4OfMZrPVOMeyrMtQCQAA5mDlBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACM4tVwU1xcrJSUFMXHxys6OlqJiYnavXu3Y/uiRYvUt29fxcTEaObMmVyKAAAA1Mir15YqKytTu3btNH/+fLVq1Urr1q3ThAkTlJaWpp07d2rZsmVatGiRgoODNX78eHXo0EGDBg3yZskAAMDHeTXchISEaOzYsY6vY2Nj9frrr+vo0aNau3atBg8erCuvvFKSNHLkSKWlpVUaboqLi1VcXOw05u/vr8DAwDrXZLfbnT5fSmFhYTXOqU0dntqPuy5n70xE/1xH79xD/9xD/1xXWe/8/Nw/qORTVwXPyMjQ2bNnFRERoSNHjig2NtaxrXPnzjp06FClt1u4cKHmzZvnNDZkyBAlJCS4XEtmZqbLt62tCw/BVeXo0aOXbT+ecjl6ZzL65zp65x765x7657oLexcVFeX2/nwm3BQWFmrKlClKTExUWFiYCgoKFBoa6tgeGhqq8+fPV3rbMWPGaMSIEU5j7qzcZGZmKiIiwiPpsTrh4eE1zsnNzb1s+3HX5eydieif6+ide+ife+if6y5V73wi3JSWlmry5MmKiIhwHKZq2LCh8vPzHXPy8/MVEhJS6e0DAwNdCjLV8fPzu+RP0ry8vFrVcbn24ymXo3cmo3+uo3fuoX/uoX+u83TvvP5dsNvtmjJlimw2m5577jnZbDZJPy1LHTx40DHv0KFD6tSpk7fKBAAA9YTXw8306dOVnZ2tGTNmyN//vwtJcXFx+vjjj3Xs2DFlZ2crNTVVcXFxXqwUAADUB149LHXy5EmtXLlSQUFB6tu3r2P8zTff1N13361HHnlEo0ePlt1u16BBg/Tggw96sVoAAFAfeDXctG3bVjt27Khy+5gxYzRmzJjLWBEAAKjvvH5YCgAAwJMINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMIq/twuA77DZbDXOsSzrMlQCAIDrWLkBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKN4NdwsX75cI0aMUM+ePTV37lzH+I4dO3TrrbfqnnvucXzs2rXLi5UCAID6wqvXlmrRooXGjRunTz/9tMK2K664QitXrrz8RQEAgHrNq+Gmd+/ekqQtW7Z4swwAAGAQn70q+OnTp3XfffcpLCxMcXFxSkpKUoMGDSqdW1xcrOLiYqcxf39/BQYG1vl+7Xa70+dLKSwsrMY5tanDV/ZzOXtnIvrnOnrnHvrnHvrnusp65+fn/hkzNsuyLLf34qbp06erefPmSk5OliSdOXNGeXl5at++vdLT0zV58mQNHDhQI0eOrPT2c+fO1bx585zGhgwZooSEhEteOwAA8JyoqCi39+GT4eZif//737V06VItWLCg0u2eXrnJzMxURESEW+kxPDzc5dteKDc31yP35an9VCc0NFRbt251u3c/V5567v0c0Tv30D/30D/XVdY7T/TQZw9LXaimBxoYGOhSkKnpPt1pcF5ensfq8MR9eWo/teFu737u6J/r6J176J976J/rPN07r34XSktLVVRUJLvdrrKyMhUVFamsrEw7duzQqVOnJEkZGRmaP3++evXq5c1SAQBAPeHVlZv58+c7nSuzYMECTZ06Vbm5uZoyZYrOnTunZs2aKS4ursrzbQAAAC7k1XCTnJxc5Xk2hBkAAOAKDg4CAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEZxKdw8+OCD+n//7/9VGP/zn/+sp59+2u2iUL+Fh4fLZrNV+wEAwKXi0jsUnzhxQs2bN68wvn37du3bt8/togAAAFxVp3CzevVqx79zcnKcvi4sLFR6eroCAgI8Vx0AAEAd1SncpKSkOA4rHD9+XNOmTXPablmWunTp4tECAQAA6qLOh6Usy5LNZpNlWU7jQUFBioyM1KRJkzxWHAAAQF3VKdx89dVXkqRbb71V119/vRYsWHBJigIAAHCVSycUz5kzR6GhoZ6uBQAAwG0uhZsePXro6NGj+vjjj/XDDz9UOEQ1duxYjxQHAABQVy6Fm7/97W+aPn16hVBTjnADAAC8xaVws2DBAtntdk/XAgAA4DaXwk12drbCwsI0b948RUVFqUGDBp6uCwAAwCUuXX7hlltuUePGjdW5c2eCDQAA8Ckurdz07dtXL774op5++mn1799fjRo1ctp+8803e6Q4AACAunIp3JS/U/GGDRu0YcMGp202m01ffvmlR4oDAACoK5fCjaQqXykFAADgTS6Fm1WrVnm6jnrFZrN5uwQAAFAFl8JN27ZtPV0HAACAR7h8zk1VbDabnn32WZcLAgAAcIdL4Wb16tWVHpopv2I44QYAAHiLS+Gme/fuTuEmLy9PBw8elM1m00033eSp2gAAAOrMpXDzzjvvVBhLT09XUlKS7rnnHreLAgAAcJVL71BcmcjISF111VVaunSpp3YJAABQZy6fc3Mhu92ujIwM7dq1S8HBwR4pDAAAwBVuvUPxxSzL4tILAADAqzz2DsXNmjXTrbfeqgkTJrhdFAAAgKtcCjdfffWVp+sAAADwCJdXbiSpqKhIhw8fliR17NhRQUFBHikKAADAVS6HmwULFmjhwoUqKiqSJAUFBenRRx9VYmKip2oDAACoM5deCv63v/1Nb7/9tgoLC2VZlizLUmFhoWbPnq20tDRP1wgAAFBrLq3cfPjhh5Kk3r17KzY2VpL097//XZs2bdLSpUs1YMAAz1UIAABQBy6Fm/T0dLVr106vvPKKY6xv374aOHCgjhw54rHiAAAA6sqlw1INGjRQUVGRSktLHWOlpaUqKipSgwYNPFYcAABAXbm0cnPVVVdp9+7dGjdunPr06SNJ2rhxo3JycnTjjTd6tEAAAIC6cCnc/PKXv9SkSZO0d+9e7d27V9J/39Rv1KhRnqsOAACgjlw6LBUdHa2UlBS1bt3a8WqpNm3a6Pnnn+eq4AAAwKvqtHJz4sQJ7dy5U5GRkYqLi1NcXJxycnIkSZmZmcrIyNCJEyfUrl27S1IsAABATeq0crNo0SJNmzbN6UTipk2bqmnTpiosLNS0adO0aNEiT9cIAABQa3UKNzt27FBoaKhuuummCttuu+02NWrUiOtOAQAAr6pTuMnKylKbNm2q3N66dWtlZWW5XRQAAICr6hRuGjRooJMnT8put1fYVlZWphMnTsjf361rcQIAALilTuEmKipKBQUFmj17doVtc+bMUX5+vqKiojxWHAAAQF3VaZmlb9+++te//qW//vWv2rp1q7p37y6bzaZvvvlG//73v2Wz2XTfffddqloBAABqVKdwk5CQoE8++UT79+/XgQMHdODAAcc2y7J09dVXKyEhweNFAgAA1FadDksFBgZqzpw5io2NlZ+fn+MN/Pz8/NS/f3/Nnj1bAQEBl6pWAACAGtX57N9GjRrphRde0OTJk5WRkSHLstShQweFhYVdivoAAADqxOWXNoWFhem6667zZC0AAABuc+naUgAAAL6KcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGMWr4Wb58uUaMWKEevbsqblz5zptS0tLU1xcnKKjo5WSkqKSkhIvVQkAAOoTr4abFi1aaNy4cYqJiXEaP3jwoP70pz/plVde0Zo1a3T69Gn95S9/8VKVAACgPvFquOndu7eio6PVqFEjp/FPP/1UMTEx6tq1q8LCwpSUlKQ1a9Z4qUoAAFCfuHzhzEvp8OHDuu222xxfd+7cWadOnVJBQYEaNmxYYX5xcbGKi4udxvz9/RUYGFjn+7bb7U6fK3M5r4BeXR3lalOPp/ZTndDQUKfP7tbzc1Ob5x4qR+/cQ//cQ/9cV1nv/PzcX3fxyXBz/vx5pz+Q5X90qwo3Cxcu1Lx585zGhgwZooSEBJdryMzMrHLb7t27Xd5vXR09erTGObWpx1P7qY2tW7fWOKc29fxcVffcQ/XonXvon3von+su7F1UVJTb+/PJcBMSEqL8/HzH13l5eZJUabCRpDFjxmjEiBFOY+6s3GRmZioiIqLK9BgeHl7n/boqNze3xjmXs57qhIaGauvWrbrjjjucvn+Vqc3jqg1PPXZP1eOO2jz3UDl65x765x7657pL1TufDDcdO3bUwYMHHV8fOnRIbdq0qTLcBAYGuhRkquPn51dlo8vD1uVQm2/25aynNvLz82usyVNPYk89dl/6hVTdcw/Vo3fuoX/uoX+u83TvvPpdKC0tVVFRkex2u8rKylRUVKSysjL1799fn332mfbt26e8vDwtWLBA8fHx3iwVAADUE15duZk/f77TuTILFizQ1KlTNWDAAE2YMEETJ05Ufn6+YmJi9Oijj3qxUgAAUF94NdwkJycrOTm50m0DBgzQgAEDLnNFAACgvuPgIAAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwik9eOBO43Gw2W41zLMu6DJUAANzFyg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAo3BVcPis2lyp29d46uriXKUcAFzHyg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3Ps5ms9X4UR+Z+rhqo6rHGx4eLkmOzwAA1xBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIzi7+0CAOBCNputxjmWZV2GSgDUV6zcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRfPryC+PGjdPevXvVoEEDSVL37t315ptverkqAADgy3w63EjSM888o7i4OG+XAQAA6gmfDze1UVxcrOLiYqcxf39/BQYG1nlfdrvd6XNlwsLC6rzfn4PQ0FCnz6ap7jlRzp3nRl37V5t66qPa9PDix16bn1tUjf65h/65rrLe+fm5f8aMzfLhy+uOGzdOhw8fliRdddVVmjBhgrp06VJh3ty5czVv3jynsSFDhighIeGy1AkAADwjKirK7X34dLjZu3evOnbsKD8/Py1dulQffPCBli9fXuF/tp5eucnMzFRERESV6TE8PLzO+/05CA0N1datW3XHHXcoPz/f2+V4XG5ubo1z3Hlu1LV/tamnNmpTs6fuqzZcqac2P7eoGv1zD/1zXWW980QPffqwVLdu3Rz/Hj16tFatWqU9e/bo9ttvd5oXGBjoUpCpjp+fX5UNzsvL8+h9mSY/P9/IHtXmB84Tj7u2/fPUL9HLeV+14U491f3comb0zz30z3We7l29+i7wpAEAADXx2bRw7tw5bdu2TcXFxSopKVFqaqrOnj3rtJoDAABwMZ89LFVaWqo///nPOnr0qPz9/XXVVVdp5syZvFIJAABUy2fDTdOmTfXee+95uwwAAFDP+OxhKQAAAFcQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARvHZN/EDfI3NZvN2CV5Tm8duWdZlqMQ30R/At7ByAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUf28XAMA1NpvN2yUAgE9i5QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUbgqOIDLhiuZV682/bEs6zJUgp+7+v5cZOUGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCj+3i4AgBlsNpu3S/BpnupPbfZjWZaR91UbnqqnNsprDgsL0+7duxUeHq68vLxLVs/l/H7Ud6zcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRfDrc5OTk6IknntDdd9+thx56SNu3b/d2SQAAwMf5dLh5+eWX1bx5c61fv15PPPGEnn76aeXm5nq7LAAA4MN8NtwUFBRo06ZNSk5OVnBwsKKjo9WpUyd9/vnn3i4NAAD4MJ+9KnhGRoYaNmyo1q1bO8Y6d+6sw4cPV5hbXFys4uJipzF/f38FBgbW+X7tdrvT58qEhYXVeb8/B6GhoU6fUTf0r/Yu/vmszc/tpeRrvxPq2oeq+lebx+Wpnnuqh5fzOVBec3U/u56sx9e+H564r8qee35+7q+7+Gy4OX/+fIUnSmhoaKWHpRYuXKh58+Y5jY0dO1bJycl1vl8/Pz9FRUVVO+fcuXN13u/PyalTp7xdQr1G/+quNj+3l1J9/51QVf8u5+Oqjz28uOZL/bNr4vfjUv3s+my4CQkJUX5+vtNYfn6+GjZsWGHumDFjNGLECKcxV1ZtAABA/eez59y0b99eBQUFysrKcowdOnRIHTt2rDA3MDBQYWFhTh+EGwAAfp58Ntw0bNhQ0dHRmjt3rgoLC7V582YdPHhQ0dHR3i4NAAD4MJtlWZa3i6hKTk6Opk6dqq+//lqtW7fWU089pZ49e3q7LAAA4MN8OtwAAADUlc8elgIAAHAF4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbi6Qk5OjJ554Qnfffbceeughbd++3dsl+azi4mKlpKQoPj5e0dHRSkxM1O7dux3bFy1apL59+yomJkYzZ84UL8qr3O7du3XrrbfqL3/5i2OM3tXOu+++q/j4ePXq1UvDhw93vKM5/avZv//9byUlJSk6OloPPvigVq5cKemn6/u89tpr6t27t/r166fU1FTvFuoDli9frhEjRqhnz56aO3eu07a0tDTFxcUpOjpaKSkpKikpcWw7duyYkpKSdNddd2nEiBHav3//5S7dJ1TVvy+++MLxHOzfv7/+9Kc/qbS01LHd7f5ZcHjqqaeslJQU6/z589amTZusmJgY68cff/R2WT6poKDAeuedd6yTJ09aZWVl1qeffmrFxMRY+fn51ubNm624uDgrMzPT+v77762EhARrxYoV3i7Z55SVlVmjR4+2Ro0aZc2bN8+yLIve1dLSpUut5ORk6+TJk5bdbrf2799vFRUV0b9aSkhIsN555x2rrKzM2rdvn3XPPfdYhw8ftj788ENr2LBhVnZ2tnX06FGrf//+1pdffuntcr1q48aN1qZNm6zJkydbc+bMcYwfOHDA6t27t7V3717r3Llz1mOPPWbNnj3bsf2Xv/ylNWfOHKuwsNBatmyZNXDgQKukpMQbD8GrqurfJ598Ym3dutU6f/689cMPP1iPPvqoNX/+fMd2d/vHys1/FBQUaNOmTUpOTlZwcLCio6PVqVMnff75594uzSeFhIRo7NixatOmjfz8/BQbG6uAgAAdPXpUa9eu1eDBg3XllVeqRYsWGjlypNauXevtkn3Oxx9/rG7dujldNI7e1aysrEwLFizQM888ozZt2shms6lLly4KDAykf7V08uRJxcbGys/PT9dcc40iIyOVnp6utWvXauTIkWrWrJnat2+vQYMGac2aNd4u16t69+6t6OhoNWrUyGn8008/VUxMjLp27aqwsDAlJSU5epWenq4jR45ozJgxCgoK0iOPPCK73a5vvvnGC4/Au6rqX//+/XX77bcrODhYTZs2VVxcnPbs2SPJM/0j3PxHRkaGGjZsqNatWzvGOnfurMOHD3uxqvojIyNDZ8+eVUREhI4cOaIuXbo4tnXu3FmHDh3yYnW+58cff9SSJUsqXLme3tUsKytLhYWFWr9+vfr166eHHnpIK1askET/amvo0KH65JNPVFpaqr179+r06dO6/vrrdfjw4Qr943dg5Srr1alTp1RQUKAjR46offv2Ttc45LlYvV27djmuHemJ/vnsVcEvt/Pnzys0NNRpLDQ0VLm5uV6qqP4oLCzUlClTlJiYqLCwMBUUFDj1MjQ0VOfPn/dihb5n9uzZGjZsWIX/zdC7mmVlZSkvL08ZGRlatWqVMjMz9dhjjykyMpL+1dKdd96pqVOnasGCBZKkKVOmqEWLFhV+D4aGhqqgoMBbZfq0i3sVFhYm6aef4YufhxLPxeps2LBB27dv15IlSyRV/D0o1b1/hJv/CAkJcZyQWC4/P18NGzb0UkX1Q2lpqSZPnqyIiAiNHTtW0k8XPb2wl/n5+QoJCfFWiT7nu+++07fffqunnnqqwjZ6V7OgoCBJ0tixYxUcHKwuXbqoX79+2rJlC/2rhdzcXP3+97/XlClT1KdPHx0+fFi/+93v1Llz5wq/B/kdWLWLe5WXlyfpp5/hi5+HEs/FquzYsUMzZszQzJkz1axZM0kVfw9Kde8fh6X+o3379iooKFBWVpZj7NChQ45lMlRkt9s1ZcoU2Ww2Pffcc7LZbJKkqKgoHTx40DHv0KFD6tSpk7fK9Dk7d+7U0aNHFRcXp9jYWK1bt05//etflZKSQu9qoUOHDgoICHA83yTx3KuDY8eOKTg4WH379lWDBg3UpUsX3XDDDfr666/VsWPHCv3jd2DlKutVmzZt1LBhQ0VFRSkzM1PFxcVO23kuOtu7d68mT56sl156Sdddd51j3BP9I9z8R8OGDRUdHa25c+eqsLBQmzdv1sGDBxUdHe3t0nzW9OnTlZ2drRkzZsjf/7+LgHFxcfr444917NgxZWdnKzU1VXFxcV6s1LeUnyOSmpqq1NRU9erVS0OGDNHEiRPpXS2EhITo3nvv1fz581VcXKwjR45o3bp1uuuuu+hfLXTo0EGFhYXatGmTLMvS4cOH9c0336hz5866//779d577yknJ0eZmZlauXKl4uPjvV2yV5WWlqqoqEh2u11lZWUqKipSWVmZ+vfvr88++0z79u1TXl6eFixY4OhVZGSkIiMjtWjRIhUXF+vjjz+WzWbTTTfd5N0H4wVV9e/gwYOaMGGCpkyZoltuucXpNp7oH1cFv0BOTo6mTp2qr7/+Wq1bt9ZTTz2lnj17erssn3Ty5EkNGDBAQUFB8vP7b0Z+88031b17dy1cuFCLFy+W3W7XoEGD9Pjjjzv9Txv/9dxzz+nKK6/Ur371K0mid7Vw7tw5TZs2TV9++aWaNGmixMREPfTQQ5LoX21s3bpVs2bN0rFjx9S4cWM98sgjSkxMlN1u1+uvv660tDQFBARo9OjRGjlypLfL9aq5c+dq3rx5TmNTp07VgAEDlJaWptmzZys/P18xMTH6wx/+4DgJNjMzU1OnTtW///1vdejQQVOnTtXVV1/tjYfgVVX1b+fOnVqzZo3jMLMkde/eXW+++aYk9/tHuAEAAEbhsBQAADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBQunAngsrrwHUsDAgK0evVqNW/e3LF91KhR+vbbbyVJ3bp106JFi5xun5aWppSUFEmSn5+fVq1apTZt2jjNufjt3AMCAtSqVSvddtttSkpKUtu2bSVJ48aN086dOyut89VXX1Xv3r1dfpwAvIeVGwBeU1JSoo8//tjx9d69ex3BpippaWmOf9vtdq1evbrKuU2aNFG3bt3Utm1bHT9+XCtWrNCjjz5a4YrDAQEB6tatm9NH48aNXXxUALyNlRsAHvfFF19o/vz5OnLkiEpKStSyZUtde+21evrpp53m+fv766OPPtKYMWPk7++vpUuXOsZLS0sr7Pf48ePatWuXJOm6667Tt99+qzVr1jiuy3Wxu+++W88995wkaebMmXrvvfeUlZWlr776ymlVpkWLFhVWiADUX6zcAPConJwcPfnkk9qzZ4/CwsLUvn175ebmat26dcrLy3OaGxMTozNnzmjDhg3Kzs7W+vXr1bp1a3Xr1q3Sfa9evVqWZal58+b64x//KOmnC+x98803l/phAahHWLkB4FGnTp1SSUmJQkNDtXz5cgUHB8uyLH377bdq2rSp09yhQ4fqH//4h5YuXaqMjAyVlJTokUce0datWyvs17IsrV27VpLUv39/XX311erSpYsOHDigtLQ03XTTTRVu88UXXygxMVHnzp3T0aNHJUktW7bUrbfe6jTv5MmTFc7T2bFjhzttAOBFhBsAHtWxY0ddccUVOn78uPr166eIiAh17txZMTEx6tq1q9Pca665RjfccIN2796tQ4cOKSgoSIMHD6403Hz99dc6fvy4JCkuLs7xeebMmVq/fr2efPJJBQcHO93mxx9/1I8//ih/f3+1a9dOPXv2VFJSkkJDQ53mBQQE6Oqrr/ZkGwB4EeEGgEcFBQVp8eLFWrNmjf71r3/p8OHDWrt2rdasWaMZM2ZUmD906FDt3r1b+fn5GjBggJo0aVLpfi88cTg5OVmSVFZWJknKz8/XZ5995gg95R544AHHOTfV4ZwbwCyccwPAo/Ly8nTkyBENHTpUzz//vFJTU3X77bdLUqUvu7733nvVokULST8FncoUFBRow4YNTveRl5en8+fPO8aqe9UUgJ8XVm4AeFROTo6SkpLUuHFjtWrVSiUlJY7zXbp06aKsrCyn+eWvmCopKaly1WbDhg2OILN06VJ16tTJsW3JkiV67bXXtGPHDp06darCe97UxpkzZ5SYmOg0Nnz4cPXr16/O+wLgfazcAPCo8PBwDRgwQM2aNdOJEyd0+vRpRUZGavz48Ro0aFCltwkNDa0y2Ej/fW+b9u3bOwUbSerTp4+kmt/zpjolJSXau3ev08eZM2dc2hcA77NZlmV5uwgAAABPYeUGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKP8f5D2521YNA+kAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")  # ARIMA generates lots of warnings\n",
    "arima_smapes, arima_time = eval_local_model(air_train, air_test, ARIMA, p=12, d=1, q=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f431b961",
   "metadata": {},
   "source": [
    "Or the Kalman Filter? (in Darts, fitting Kalman filters uses the N4SID system identification algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3c82dbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07ccda2d49034473b80c4a577a07b79d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/245 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kf_smapes, kf_time = eval_local_model(air_train, air_test, KalmanForecaster, dim_x=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb1a576",
   "metadata": {},
   "source": [
    "### Comparing models\n",
    "\n",
    "Below, we define a function that will be useful to visualise how models compare to each other in terms of median sMAPE, and time required to obtain the forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d47e85f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_models(method_to_elapsed_times, method_to_smapes):\n",
    "    shapes = [\"o\", \"s\", \"*\"]\n",
    "    colors = [\"tab:blue\", \"tab:orange\", \"tab:green\", \"tab:red\", \"tab:purple\"]\n",
    "    styles = list(product(shapes, colors))\n",
    "\n",
    "    plt.figure(figsize=(6, 6), dpi=100)\n",
    "    for i, method in enumerate(method_to_elapsed_times.keys()):\n",
    "        t = method_to_elapsed_times[method]\n",
    "        s = styles[i]\n",
    "        plt.semilogx(\n",
    "            [t],\n",
    "            [np.median(method_to_smapes[method])],\n",
    "            s[0],\n",
    "            color=s[1],\n",
    "            label=method,\n",
    "            markersize=13,\n",
    "        )\n",
    "    plt.xlabel(\"elapsed time [s]\")\n",
    "    plt.ylabel(\"median sMAPE over all series\")\n",
    "    plt.legend(bbox_to_anchor=(1.4, 1.0), frameon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef4336ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes = {\n",
    "    \"naive-last\": naive1_smapes,\n",
    "    \"naive-seasonal\": naive12_smapes,\n",
    "    \"Exponential Smoothing\": ets_smapes,\n",
    "    \"Theta\": theta_smapes,\n",
    "    \"ARIMA\": arima_smapes,\n",
    "    \"Kalman Filter\": kf_smapes,\n",
    "}\n",
    "\n",
    "elapsed_times = {\n",
    "    \"naive-last\": naive1_time,\n",
    "    \"naive-seasonal\": naive12_time,\n",
    "    \"Exponential Smoothing\": ets_time,\n",
    "    \"Theta\": theta_time,\n",
    "    \"ARIMA\": arima_time,\n",
    "    \"Kalman Filter\": kf_time,\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times, smapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8df1cd5",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "ARIMA gives the best results, but it is also (by far) the most time-consuming model. The Exponential Smoothing model provides an interesting tradeoff, with good forecasting accuracy and lower computational cost. Can we maybe find a better compromise by considering *global* models - i.e., models that are trained only once, jointly on all time series?\n",
    "\n",
    "## Part 2: Global models on the `air` dataset\n",
    "In this section we will use \"global models\" - that is, models that are fit on multiple series at once. Darts has essentially two kinds of global models:\n",
    "\n",
    "* `SKLearnModels` which are wrappers around sklearn-like regression models (Part 2.1).\n",
    "* PyTorch-based models, which offer various deep learning models (Part 2.2).\n",
    "\n",
    "Both models can be trained on multiple series by \"tabularizing\" the data - i.e., taking many (input, output) sub-slices from all the training series, and training machine learning models in a supervised fashion to predict the output based on the input.\n",
    "\n",
    "We start by defining a function `eval_global_model()` which works similarly to `eval_local_model()`, but on global models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea804331",
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_global_model(\n",
    "    train_series: list[TimeSeries], test_series: list[TimeSeries], model_cls, **kwargs\n",
    ") -> tuple[list[float], float]:\n",
    "    start_time = time.time()\n",
    "\n",
    "    model = model_cls(**kwargs)\n",
    "    model.fit(train_series)\n",
    "    preds = model.predict(n=HORIZON, series=train_series)\n",
    "\n",
    "    elapsed_time = time.time() - start_time\n",
    "\n",
    "    smapes = eval_forecasts(preds, test_series)\n",
    "    return smapes, elapsed_time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76969990",
   "metadata": {},
   "source": [
    "### Part 2.1: Using Darts `SKLearnModel`s.\n",
    "`SKLearnModel` in Darts are forecasting models that can wrap around any \"scikit-learn compatible\" regression model to obtain forecasts. Compared to deep learning, they represent good \"go-to\" global models because they typically don't have many hyper-parameters and can be faster to train. In addition, Darts also offers some \"pre-packaged\" regression models such as `LinearRegressionModel` and `LightGBMModel`.\n",
    "\n",
    "We'll now use our function `eval_global_models()`. In the following cells, we will try using some regression models, for example:\n",
    "\n",
    "* `LinearRegressionModel`\n",
    "* `LightGBMModel`\n",
    "* `SKLearnModel(some_sklearn_model)`\n",
    "\n",
    "You can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sklearn_model.html) for how to use them.\n",
    "\n",
    "Important parameters are `lags` and `output_chunk_length`. They determine respectively the length of the lookback and \"lookforward\" windows used by the model, and they correspond to the lengths of the input/output subslices used for training. For instance `lags=24` and `output_chunk_length=12` mean that the model will consume the past 24 lags in order to predict the next 12. In our case, because the shortest training series has length 36, we must have `lags + output_chunk_length <= 36`. (Note that `lags` can also be a list of integers representing the individual lags to be consumed by the model instead of the window length)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c09d538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_smapes, lr_time = eval_global_model(\n",
    "    air_train, air_test, LinearRegressionModel, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d491dac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Some label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003476 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 7649\n",
      "[LightGBM] [Info] Number of data points in the train set: 30397, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.481005\n",
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_smapes, lgbm_time = eval_global_model(\n",
    "    air_train, air_test, LightGBMModel, lags=30, output_chunk_length=1, objective=\"mape\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c56f3f5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_smapes, rf_time = eval_global_model(\n",
    "    air_train, air_test, RandomForestModel, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d1f2b3",
   "metadata": {},
   "source": [
    "### Part 2.2: Using deep learning\n",
    "Below, we will train an N-BEATS model on our `air` dataset. Again, you can refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html) for documentation on the hyper-parameters.\n",
    "The following hyper-parameters should be a good starting point, and training should take in the order of a minute or two if you're using a (somewhat slow) Colab GPU.\n",
    "\n",
    "During training, you can have a look at the [N-BEATS paper](https://arxiv.org/abs/1905.10437)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17ca9d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Possible N-BEATS hyper-parameters\n",
    "\n",
    "# Slicing hyper-params:\n",
    "IN_LEN = 30\n",
    "OUT_LEN = 4\n",
    "\n",
    "# Architecture hyper-params:\n",
    "NUM_STACKS = 20\n",
    "NUM_BLOCKS = 1\n",
    "NUM_LAYERS = 2\n",
    "LAYER_WIDTH = 136\n",
    "COEFFS_DIM = 11\n",
    "\n",
    "# Training settings:\n",
    "LR = 1e-3\n",
    "BATCH_SIZE = 1024\n",
    "MAX_SAMPLES_PER_TS = 10\n",
    "NUM_EPOCHS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731992cd",
   "metadata": {},
   "source": [
    "Let's now build, train and predict using an N-BEATS model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c9de4c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name          | Type             | Params\n",
      "---------------------------------------------------\n",
      "0 | criterion     | SmapeLoss        | 0     \n",
      "1 | train_metrics | MetricCollection | 0     \n",
      "2 | val_metrics   | MetricCollection | 0     \n",
      "3 | stacks        | ModuleList       | 525 K \n",
      "---------------------------------------------------\n",
      "523 K     Trainable params\n",
      "1.9 K     Non-trainable params\n",
      "525 K     Total params\n",
      "2.102     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4f98e6788364a6db49ddc81669b1531",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0010ee2296784847b88da8157127fe32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGCdQF8XcDlzOBxVjjHG1EElAADYhRkYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYx6cBpqioSNOmTVNiYqJiYmKUnJys3bt3u9YvXrxYcXFxio2N1axZs2SM8WG1AADAXwT68sFLS0vVqlUrLViwQM2aNdOGDRs0fvx4paena+fOnVqxYoUWL16s0NBQPfLII2rbtq0GDRrky5IBAIAf8OkMTFhYmMaOHasWLVooICBA8fHxCgoK0uHDh7V+/XoNHjxYV111laKiojRy5EitX7/el+UCAAA/4dMZmItlZmbqzJkzat26tTIyMhQfH+9a17FjRx04cKDc7YqKilRUVOS2LDAwUMHBwWXGOp1Ot6++FBERUeUYf6hT8q++2YS+eYa+1Rw98wx980xt9i0goHpzK34TYAoKCjR58mQlJycrIiJC+fn5Cg8Pd60PDw/XuXPnyt120aJFmj9/vtuyoUOHKikpqcLHy8rK8k7hl+DC830qcvjw4TqopPr8oW82om+eoW81R888Q988Uxt9a9euXbXG+UWAKSkp0aRJk9S6dWuNHTtWklS/fn3l5eW5xuTl5SksLKzc7ceMGaMRI0a4LatsBiYrK0utW7eudsqrLZGRkVWOycnJqYNKquZPfbMJffMMfas5euYZ+uYZf+ibzwOM0+nU5MmT5XA4NHXqVDkcDkk/J7D9+/crJiZGknTgwAF16NCh3PsIDg4uN6xUJiAgwOcv1tzc3CrH+LrGi/lD32xE3zxD32qOnnmGvnnGl33z+bM1Y8YMnT59WjNnzlRg4L/zVEJCglatWqUjR47o9OnTSktLU0JCgg8rBQAA/sKnMzDHjx/XmjVrFBISori4ONfyV155RXfccYeGDBmi0aNHy+l0atCgQbr33nt9WC0AAPAXPg0wLVu21I4dOypcP2bMGI0ZM6YOKwIAADbw+SEkAACAmiLAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAuEw6H45JukZGRvt4FAABcCDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6wT6ugAbORwOX5cAAMBljRkYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWMenAWblypUaMWKEevbsqblz57qW79ixQ927d9edd97puu3atcuHlQIAAH8S6MsHj4qK0rhx4/T++++XWXfllVdqzZo1dV8UAADwez4NML1795Ykbd261ZdlAAAAy/g0wFTm+++/V9++fRUREaGEhASlpKSoXr165Y4tKipSUVGR27LAwEAFBweXGet0Ot2+eiIiIsLjbWvqUuq80KXWHB4eLsl79VwuvPF6uxzRt5qjZ56hb56pzb4FBFTv7BaHMcZ4/dFraMaMGWrSpIlSU1MlSadOnVJubq7atGmjQ4cOadKkSRo4cKBGjhxZ7vZz587V/Pnz3ZYNHTpUSUlJtV47AADwnnbt2lVrnF8GmIt98MEHWr58uRYuXFju+prOwGRlZal169bVTnkXi4yM9Gg7T+Tk5Hjlfi615vDwcG3btu2S+nY58sbr7XJE32qOnnmGvnmmNvtW3fvz20NIF6pqZ4KDg8sNK1Xdp6dNz83N9Wg7T3jrheGtmi+lb5cz+uYZ+lZz9Mwz9M0zvuybT5+tkpISFRYWyul0qrS0VIWFhSotLdWOHTt04sQJSVJmZqYWLFigu+66y5elAgAAP+LTGZgFCxa4nbuycOFCTZkyRTk5OZo8ebLOnj2rK664QgkJCRWe/wIAAC4/Pg0wqampFZ73QmABAAAV4YAfAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFjHik+jvpw5HI4qxxhj6qCSn0VGRnrlk63rsmYAwC8PMzAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOh4FmHvvvVdPPPFEmeWvvfaannzyyUsuCgAAoDKBnmx07NgxNWnSpMzyzz77THv37r3kogAAACpTowCzdu1a17+zs7Pdvi8oKNChQ4cUFBTkveoAAADKUaMAM23aNDkcDjkcDh09elTTp093W2+MUadOnbxaIAAAwMVqfAjJGCOHwyFjjNvykJAQRUdHa8KECV4rDgAAoDw1CjCff/65JKl79+664YYbtHDhwlopCgAAoDIencQ7Z84chYeHe7sWAACAavEowHTr1k2HDx/WqlWr9OOPP5Y5nDR27FivFAcAAFAejwLMP/7xD82YMaNMcDmPAAMAAGqTRwFm4cKFcjqd3q4FAACgWjwKMKdPn1ZERITmz5+vdu3aqV69et6uCwAAoEIefZTALbfcooYNG6pjx46EFwAAUOc8moGJi4vTc889pyeffFL9+/dXgwYN3Nb/x3/8h1eKAwAAKI9HAeb8O/Ju2rRJmzZtclvncDj06aefeqU4AACA8ngUYCRVeAUSAABAbfMowLz77rvergMAAKDaPAowLVu29HYdAAAA1ebxOTAVcTgcevrppz0uCAAAoCoeBZi1a9fK4XCUWX7+k6oJMAAAoDZ5FGC6du3qFmByc3O1f/9+ORwO3Xzzzd6qDQAAoFweBZh58+aVWXbo0CGlpKTozjvvvOSiAAAAKuPRO/GWJzo6Wp07d9by5cu9dZcAAADl8vgcmAs5nU5lZmZq165dCg0N9UphAAAAFbmkd+K9mDGGjxEAAAC1zmvvxHvFFVeoe/fuGj9+/CUXBQAAUBmPAsznn3/u7ToAAACqzeMZGEkqLCzUwYMHJUnt27dXSEiIV4oCAACojMcBZuHChVq0aJEKCwslSSEhIXrooYeUnJzsrdoAAADK5dFl1P/4xz/0+uuvq6CgQMYYGWNUUFCg2bNnKz093ds1AgAAuPFoBubtt9+WJPXu3Vvx8fGSpA8++EBbtmzR8uXLNWDAAO9VCAAAcBGPAsyhQ4fUqlUrvfDCC65lcXFxGjhwoDIyMrxWHAAAQHk8OoRUr149FRYWqqSkxLWspKREhYWFqlevnteKAwAAKI9HMzCdO3fW7t27NW7cOPXp00eStHnzZmVnZ+umm27yaoEAAAAX8yjAPPjgg5owYYL27NmjPXv2SPr3G9uNGjXKe9UBAACUw6NDSDExMZo2bZqaN2/uugqpRYsWeuaZZ/g0agAAUOtqNANz7Ngx7dy5U9HR0UpISFBCQoKys7MlSVlZWcrMzNSxY8fUqlWrWikWAABAquEMzOLFizV9+nS3k3cbN26sxo0bq6CgQNOnT9fixYu9XSMAAICbGgWYHTt2KDw8XDfffHOZdT169FCDBg34nCQAAFDrahRgTp48qRYtWlS4vnnz5jp58uQlFwUAAFCZGgWYevXq6fjx43I6nWXWlZaW6tixYwoMvKTPhwQAAKhSjQJMu3btlJ+fr9mzZ5dZN2fOHOXl5aldu3ZeKw4AAKA8NZouiYuL09dff62///3v2rZtm7p27SqHw6Evv/xS3377rRwOh/r27VtbtQIAAEiq4QxMUlKSOnfuLGOM9u3bp7ffflvLly/Xt99+K2OMOnfurKSkpGrf38qVKzVixAj17NlTc+fOdVuXnp6uhIQE13vOFBcX16RUAADwC1ajABMcHKw5c+YoPj5eAQEBrjexCwgIUP/+/TV79mwFBQVV+/6ioqI0btw4xcbGui3fv3+/Xn75Zb3wwgtat26dvv/+e/3tb3+rSakAAOAXrMZn3DZo0EDPPvusJk2apMzMTBlj1LZtW0VERNT4wXv37i1J2rp1q9vy999/X7Gxsbr++uslSSkpKZo6daoefvjhGj8GAAD45fH4kqGIiAhdd9113qzF5eDBg+rRo4fr+44dO+rEiRPKz89X/fr1y4wvKipSUVGR27LAwEAFBweXGXv+CqryrqSqLk/CWm2qzr5cas3h4eFuXy/VpfTfJt54vV2O6FvN0TPP0DfP1GbfAgKqd3DIL695PnfunNsfyvN/fCsKMIsWLdL8+fPdlg0dOrTS83GysrI8rm/37t0eb1sbDh8+XOUYb9W8bds2r9xPdWr+JbmU19vljL7VHD3zDH3zTG30rbpXM/tlgAkLC1NeXp7r+9zcXEkqN7xI0pgxYzRixAi3ZZXNwGRlZal169bVTnkXi4yM9Gi72pKTk1PlmEutOTw8XNu2bdOtt97q9tzUpursl7/zxuvtckTfao6eeYa+ecYf+uaXAaZ9+/bav3+/6/sDBw6oRYsWFQaY4ODgcsNKZQICAjxu+vlA5S+qsx/eqjkvL6/O9v+X9MvkUl5vlzP6VnP0zDP0zTO+7JtPn62SkhIVFhbK6XSqtLRUhYWFKi0tVf/+/fXhhx9q7969ys3N1cKFC5WYmOjLUgEAgB/x6QzMggUL3M5dWbhwoaZMmaIBAwZo/Pjxeuyxx5SXl6fY2Fg99NBDPqwUAAD4E58GmNTUVKWmppa7bsCAARowYEAdVwQAAGzAAT8AAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsI5ffho1asbhcPi6BJ+pzr4bY+qgEgBAXWIGBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGCdQF8XANjC4XBUOcYYUweVAACYgQEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADv+VwOKq8+ZuL64uMjJQkRUZG+m3NAGAjAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOoG+LgBAWQ6Ho8oxxpg6qAQA/BMzMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwjl9/FtK4ceO0Z88e1atXT5LUtWtXvfLKKz6uCgAA+JpfBxhJeuqpp5SQkODrMgAAgB/hEBIAALCO38/AvPzyy3r55ZfVuXNnjR8/Xp06dSozpqioSEVFRW7LAgMDFRwcXGas0+l0++qJiIgIj7e1VXh4uNtXm1zKc30hT5738vpWnXqq81je2i9/5I2f08sNPfMMffNMbfYtIKB6cysOY4zx+qN7yZ49e9S+fXsFBARo+fLleuutt7Ry5coyf0Tnzp2r+fPnuy0bOnSokpKS6rJcAABwidq1a1etcX4dYC52//336/HHH1evXr3cltd0BiYrK0utW7eudsq7WGRkpEfb2Sw8PFzbtm3Trbfeqry8PF+XUyM5OTlVjqmt57S8vvmynvJUp5665o2f08sNPfMMffNMbfatuvfn94eQLlTRTgUHB5cbVqq6L0+bnpub69F2vwR5eXnW7X91nufa3qcL++YP9VzIn39pX8rP6eWKnnmGvnnGl33z22fr7Nmz2r59u4qKilRcXKy0tDSdOXNGXbp08XVpAADAx/x2BqakpESvvfaaDh8+rMDAQHXu3FmzZs26LE+gBQAA7vw2wDRu3FhLlizxdRkAAMAP+e0hJAAAgIoQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgw+MVzOBxV3uA/HA6HIiMjJUmRkZE8XwDKRYABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWCfQ1wUAlxuHw+HrEmpFdfbLGFMHlXjXL3W/ANsxAwMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGQJ1xOBxV3uryfmxU0f5GRkZKkuurp/dzOfQQvwwEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1An1dAADfcjgcvi6hVlRnv4wxdfZY1eFv9Xirh3X5XHiLjTVLdfecRUREaPfu3dWuqzYwAwMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6/h1gMnOztajjz6qO+64Q/fdd58+++wzX5cEAAD8gF8HmD/96U9q0qSJNm7cqEcffVRPPvmkcnJyfF0WAADwMb8NMPn5+dqyZYtSU1MVGhqqmJgYdejQQR999JGvSwMAAD4W6OsCKpKZman69eurefPmrmUdO3bUwYMHy4wtKipSUVGR27LAwEAFBweXGet0Ot2+eiIiIsLjbW0VHh7u9hXVQ988U1d9q87vgbr8eb+Uenz1WvNWDy/ld/KlqOhvgj/XXBlv1V3V/Zx/ndVGDwICqje34rcB5ty5c2V+EMPDw8s9hLRo0SLNnz/fbdnYsWOVmppaZmxAQIDatWt3SbWdPXv2kra32YkTJ3xdgpXom2f8oW/+9vNeVT3+0LOL+VsPL1TR3wR/rrky3qrbhv332wATFhamvLw8t2V5eXmqX79+mbFjxozRiBEj3JaVN/sCAAB+Gfz2HJg2bdooPz9fJ0+edC07cOCA2rdvX2ZscHCwIiIi3G4EGAAAfrn8NsDUr19fMTExmjt3rgoKCvTxxx9r//79iomJ8XVpAADAxxzGGOPrIiqSnZ2tKVOm6IsvvlDz5s01ceJE9ezZ09dlAQAAH/PrAAMAAFAevz2EBAAAUBECDAAAsA4BBgAAWIcAAwAArEOAAQAA1rmsAkx2drYeffRR3XHHHbrvvvv02Wef+bokv1RUVKRp06YpMTFRMTExSk5O1u7du13rFy9erLi4OMXGxmrWrFniQjZ3u3fvVvfu3fW3v/3NtYyeVe6NN95QYmKi7rrrLg0fPtz1Ltz0rWLffvutUlJSFBMTo3vvvVdr1qyR9PNn07z00kvq3bu3+vXrp7S0NN8W6kMrV67UiBEj1LNnT82dO9dtXXp6uhISEhQTE6Np06apuLjYte7IkSNKSUnR7bffrhEjRui7776r69J9qqK+ffLJJ67XXP/+/fXyyy+rpKTEtb7O+2YuIxMnTjTTpk0z586dM1u2bDGxsbHmp59+8nVZfic/P9/MmzfPHD9+3JSWlpr333/fxMbGmry8PPPxxx+bhIQEk5WVZX744QeTlJRkVq9e7euS/UZpaakZPXq0GTVqlJk/f74xxtCzKixfvtykpqaa48ePG6fTab777jtTWFhI36qQlJRk5s2bZ0pLS83evXvNnXfeaQ4ePGjefvttM2zYMHP69Glz+PBh079/f/Ppp5/6ulyf2Lx5s9myZYuZNGmSmTNnjmv5vn37TO/evc2ePXvM2bNnzcMPP2xmz57tWv/ggw+aOXPmmIKCArNixQozcOBAU1xc7Itd8ImK+vbee++Zbdu2mXPnzpkff/zRPPTQQ2bBggWu9XXdt8tmBiY/P19btmxRamqqQkNDFRMTow4dOuijjz7ydWl+JywsTGPHjlWLFi0UEBCg+Ph4BQUF6fDhw1q/fr0GDx6sq666SlFRURo5cqTWr1/v65L9xqpVq9SlSxe3D4ejZxUrLS3VwoUL9dRTT6lFixZyOBzq1KmTgoOD6VsVjh8/rvj4eAUEBOiaa65RdHS0Dh06pPXr12vkyJG64oor1KZNGw0aNEjr1q3zdbk+0bt3b8XExKhBgwZuy99//33Fxsbq+uuvV0REhFJSUlw9OnTokDIyMjRmzBiFhIRoyJAhcjqd+vLLL32wB75RUd/69++vXr16KTQ0VI0bN1ZCQoK++uorSb7p22UTYDIzM1W/fn01b97ctaxjx446ePCgD6uyQ2Zmps6cOaPWrVsrIyNDnTp1cq3r2LGjDhw44MPq/MdPP/2kZcuWlfkUdHpWsZMnT6qgoEAbN25Uv379dN9992n16tWS6FtVHnjgAb333nsqKSnRnj179P333+uGG27QwYMHy/SN33PuyuvRiRMnlJ+fr4yMDLVp08bt8/R47ZVv165drs8n9EXf/PbTqL3t3LlzCg8Pd1sWHh6unJwcH1Vkh4KCAk2ePFnJycmKiIhQfn6+Wx/Dw8N17tw5H1boP2bPnq1hw4aV+V8LPavYyZMnlZubq8zMTL377rvKysrSww8/rOjoaPpWhdtuu01TpkzRwoULJUmTJ09WVFRUmd914eHhys/P91WZfuniHkVEREj6+Wf14tedxGuvPJs2bdJnn32mZcuWSSr7e06q/b5dNgEmLCzMdWLgeXl5eapfv76PKvJ/JSUlmjRpklq3bq2xY8dK+vlDNi/sY15ensLCwnxVot/417/+pW+++UYTJ04ss46eVSwkJESSNHbsWIWGhqpTp07q16+ftm7dSt8qkZOTo9///veaPHmy+vTpo4MHD+q3v/2tOnbsWOZ3Hb/nyrq4R7m5uZJ+/lm9+HUn8dq72I4dOzRz5kzNmjVLV1xxhaSyv+ek2u/bZXMIqU2bNsrPz9fJkyddyw4cOOCa/oI7p9OpyZMny+FwaOrUqXI4HJKkdu3aaf/+/a5xBw4cUIcOHXxVpt/YuXOnDh8+rISEBMXHx2vDhg36+9//rmnTptGzSrRt21ZBQUGu15ckXmvVcOTIEYWGhiouLk716tVTp06ddOONN+qLL75Q+/bty/SN33PuyutRixYtVL9+fbVr105ZWVkqKipyW89r72d79uzRpEmT9Pzzz+u6665zLfdF3y6bAFO/fn3FxMRo7ty5Kigo0Mcff6z9+/crJibG16X5pRkzZuj06dOaOXOmAgP/PVGXkJCgVatW6ciRIzp9+rTS0tKUkJDgw0r9w/lzN9LS0pSWlqa77rpLQ4cO1WOPPUbPKhEWFqZf/epXWrBggYqKipSRkaENGzbo9ttvp2+VaNu2rQoKCrRlyxYZY3Tw4EF9+eWX6tixo+6++24tWbJE2dnZysrK0po1a5SYmOjrkn2ipKREhYWFcjqdKi0tVWFhoUpLS9W/f399+OGH2rt3r3Jzc7Vw4UJXj6KjoxUdHa3FixerqKhIq1atksPh0M033+zbnalDFfVt//79Gj9+vCZPnqxbbrnFbRtf9O2y+jTq7OxsTZkyRV988YWaN2+uiRMnqmfPnr4uy+8cP35cAwYMUEhIiAIC/p1xX3nlFXXt2lWLFi3Sm2++KafTqUGDBul3v/ud2/+gIU2dOlVXXXWVfv3rX0sSPavE2bNnNX36dH366adq1KiRkpOTdd9990mib5XZtm2bXn31VR05ckQNGzbUkCFDlJycLKfTqT//+c9KT09XUFCQRo8erZEjR/q6XJ+YO3eu5s+f77ZsypQpGjBggNLT0zV79mzl5eUpNjZWf/jDH1wnoGZlZWnKlCn69ttv1bZtW02ZMkVXX321L3bBJyrq286dO7Vu3TrXoV9J6tq1q1555RVJdd+3yyrAAACAX4bL5hASAAD45SDAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsc9l8mCMA/3Dhu3wGBQVp7dq1atKkiWv9qFGj9M0330iSunTposWLF7ttn56ermnTpkmSAgIC9O6776pFixZuYy5+m/OgoCA1a9ZMPXr0UEpKilq2bClJGjdunHbu3FlunS+++KJ69+7t8X4CqF3MwADwmeLiYq1atcr1/Z49e1zhpSLp6emufzudTq1du7bCsY0aNVKXLl3UsmVLHT16VKtXr9ZDDz1U5lNzg4KC1KVLF7dbw4YNPdwrAHWBGRgAXvfJJ59owYIFysjIUHFxsZo2baprr71WTz75pNu4wMBAvfPOOxozZowCAwO1fPly1/KSkpIy93v06FHt2rVLknTdddfpm2++0bp161yfOXWxO+64Q1OnTpUkzZo1S0uWLNHJkyf1+eefu82uREVFlZnpAeDfmIEB4FXZ2dl6/PHH9dVXXykiIkJt2rRRTk6ONmzYoNzcXLexsbGxOnXqlDZt2qTTp09r48aNat68ubp06VLufa9du1bGGDVp0kR//OMfJf38AXJffvllbe8WAD/DDAwArzpx4oSKi4sVHh6ulStXKjQ0VMYYffPNN2rcuLHb2AceeED/+7//q+XLlyszM1PFxcUaMmSItm3bVuZ+jTFav369JKl///66+uqr1alTJ+3bt0/p6em6+eaby2zzySefKDk5WWfPntXhw4clSU2bNlX37t3dxh0/frzMeTM7duy4lDYAqGUEGABe1b59e1155ZU6evSo+vXrp9atW6tjx46KjY3V9ddf7zb2mmuu0Y033qjdu3frwIEDCgkJ0eDBg8sNMF988YWOHj0qSUpISHB9nTVrljZu3KjHH39coaGhbtv89NNP+umnnxQYGKhWrVqpZ8+eSklJUXh4uNu4oKAgXX311d5sA4BaRoAB4FUhISF68803tW7dOn399dc6ePCg1q9fr3Xr1mnmzJllxj/wwAPavXu38vLyNGDAADVq1Kjc+73wZN3U1FRJUmlpqSQpLy9PH374oSvYnHfPPfe4zoGpDOfAAPbhHBgAXpWbm6uMjAw98MADeuaZZ5SWlqZevXpJUrmXLP/qV79SVFSUpJ/DTHny8/O1adMmt8fIzc3VuXPnXMsquxoJwC8PMzAAvCo7O1spKSlq2LChmjVrpuLiYtf5J506ddLJkyfdxp+/Eqm4uLjC2ZdNmza5wsry5cvVoUMH17ply5bppZde0o4dO3TixIky7wlTHadOnVJycrLbsuHDh6tfv341vi8AdYMZGABeFRkZqQEDBuiKK67QsWPH9P333ys6OlqPPPKIBg0aVO424eHhFYYX6d/v/dKmTRu38CJJffr0kVT1e8JUpri4WHv27HG7nTp1yqP7AlA3HMYY4+siAAAAaoIZGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACs8/8ApDlLboZ7kpoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reproducibility\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "nbeats_model_air = NBEATSModel(\n",
    "    input_chunk_length=IN_LEN,\n",
    "    output_chunk_length=OUT_LEN,\n",
    "    num_stacks=NUM_STACKS,\n",
    "    num_blocks=NUM_BLOCKS,\n",
    "    num_layers=NUM_LAYERS,\n",
    "    layer_widths=LAYER_WIDTH,\n",
    "    expansion_coefficient_dim=COEFFS_DIM,\n",
    "    loss_fn=SmapeLoss(),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    optimizer_kwargs={\"lr\": LR},\n",
    "    pl_trainer_kwargs={\n",
    "        \"enable_progress_bar\": True,\n",
    "        # change this one to \"gpu\" if your notebook does run in a GPU environment:\n",
    "        \"accelerator\": \"cpu\",\n",
    "    },\n",
    ")\n",
    "\n",
    "nbeats_model_air.fit(air_train, dataloader_kwargs={\"num_workers\": 4}, epochs=NUM_EPOCHS)\n",
    "\n",
    "# get predictions\n",
    "nb_preds = nbeats_model_air.predict(series=air_train, n=HORIZON)\n",
    "nbeats_elapsed_time = time.time() - start_time\n",
    "\n",
    "nbeats_smapes = eval_forecasts(nb_preds, air_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf484aa",
   "metadata": {},
   "source": [
    "Let's now look again at our errors -vs- time plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "51a1a199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_2 = {\n",
    "    **smapes,\n",
    "    **{\n",
    "        \"Linear Regression\": lr_smapes,\n",
    "        \"LGBM\": lgbm_smapes,\n",
    "        \"Random Forest\": rf_smapes,\n",
    "        \"NBeats\": nbeats_smapes,\n",
    "    },\n",
    "}\n",
    "\n",
    "elapsed_times_2 = {\n",
    "    **elapsed_times,\n",
    "    **{\n",
    "        \"Linear Regression\": lr_time,\n",
    "        \"LGBM\": lgbm_time,\n",
    "        \"Random Forest\": rf_time,\n",
    "        \"NBeats\": nbeats_elapsed_time,\n",
    "    },\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_2, smapes_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83cae14c",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "So it looks like a linear regression model trained jointly on all series is now providing the best tradeoff between accuracy and speed. Linear regression is often the way to go!\n",
    "\n",
    "Our deep learning model N-BEATS is not doing great. Note that we haven't tried to tune it to this problem explicitly, doing so might have produced more accurate results. Instead of spending time tuning it though, in the next part we will see if it can do better by being trained on an entirely different dataset.\n",
    "\n",
    "## Part 3: Training an N-BEATS model on `m4` dataset and use it to forecast `air` dataset\n",
    "Deep learning models often do better when trained on *large* datasets. Let's try to load all 48,000 monthly time series in the M4 dataset and train our model once more on this larger dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1a48ae31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8c95030fa9b4f7685d29e9b7b89977e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/48000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There are 47087 monthly series in the M3 dataset\n",
      "scaling...\n",
      "done. There are 47087 series, with average training length 201.25202285131778\n"
     ]
    }
   ],
   "source": [
    "m4_train, m4_test = load_m4()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7df8178",
   "metadata": {},
   "source": [
    "We can start from the same hyper-parameters as before. \n",
    "\n",
    "With 48,000 M4 training series being on average ~200 time steps long, we would end up with ~10M training samples. With such a number of training samples, each epoch would take too long. So here, we'll limit the number of training samples used per series. This is done when calling `fit()` with the parameter `max_samples_per_ts`. We add a new hyper-parameter `MAX_SAMPLES_PER_TS` to capture this.\n",
    "\n",
    "Since the M4 training series are all slightly longer, we can also use a slightly longer `input_chunk_length`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83b26117",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing hyper-params:\n",
    "IN_LEN = 36\n",
    "OUT_LEN = 4\n",
    "\n",
    "# Architecture hyper-params:\n",
    "NUM_STACKS = 20\n",
    "NUM_BLOCKS = 1\n",
    "NUM_LAYERS = 2\n",
    "LAYER_WIDTH = 136\n",
    "COEFFS_DIM = 11\n",
    "\n",
    "# Training settings:\n",
    "LR = 1e-3\n",
    "BATCH_SIZE = 1024\n",
    "MAX_SAMPLES_PER_TS = (\n",
    "    10  # <-- new parameter, limiting the number of training samples per series\n",
    ")\n",
    "NUM_EPOCHS = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9564e32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name          | Type             | Params\n",
      "---------------------------------------------------\n",
      "0 | criterion     | SmapeLoss        | 0     \n",
      "1 | train_metrics | MetricCollection | 0     \n",
      "2 | val_metrics   | MetricCollection | 0     \n",
      "3 | stacks        | ModuleList       | 543 K \n",
      "---------------------------------------------------\n",
      "541 K     Trainable params\n",
      "1.9 K     Non-trainable params\n",
      "543 K     Total params\n",
      "2.173     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89a6e2a27432420eb8fea47ac953831f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=5` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NBEATSModel(generic_architecture=True, num_stacks=20, num_blocks=1, num_layers=2, layer_widths=136, expansion_coefficient_dim=11, trend_polynomial_degree=2, dropout=0.0, activation=ReLU, input_chunk_length=36, output_chunk_length=4, batch_size=1024, loss_fn=SmapeLoss(), optimizer_kwargs={'lr': 0.001}, pl_trainer_kwargs={'enable_progress_bar': True, 'accelerator': 'cpu'})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reproducibility\n",
    "np.random.seed(42)\n",
    "torch.manual_seed(42)\n",
    "\n",
    "nbeats_model_m4 = NBEATSModel(\n",
    "    input_chunk_length=IN_LEN,\n",
    "    output_chunk_length=OUT_LEN,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_stacks=NUM_STACKS,\n",
    "    num_blocks=NUM_BLOCKS,\n",
    "    num_layers=NUM_LAYERS,\n",
    "    layer_widths=LAYER_WIDTH,\n",
    "    expansion_coefficient_dim=COEFFS_DIM,\n",
    "    loss_fn=SmapeLoss(),\n",
    "    optimizer_kwargs={\"lr\": LR},\n",
    "    pl_trainer_kwargs={\n",
    "        \"enable_progress_bar\": True,\n",
    "        # change this one to \"gpu\" if your notebook does run in a GPU environment:\n",
    "        \"accelerator\": \"cpu\",\n",
    "    },\n",
    ")\n",
    "\n",
    "# Train\n",
    "nbeats_model_m4.fit(\n",
    "    m4_train,\n",
    "    dataloader_kwargs={\"num_workers\": 4},\n",
    "    epochs=NUM_EPOCHS,\n",
    "    max_samples_per_ts=MAX_SAMPLES_PER_TS,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e684e38",
   "metadata": {},
   "source": [
    "We can now use our M4-trained model to get forecasts for the air passengers series. As we use the model in a \"meta learning\" (or transfer learning) way here, we will be timing only the inference part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ab409a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "220454be10a74a9799a06ad35dbeabff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "preds = nbeats_model_m4.predict(series=air_train, n=HORIZON)  # get forecasts\n",
    "nbeats_m4_elapsed_time = time.time() - start_time\n",
    "\n",
    "nbeats_m4_smapes = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b22aba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_3 = {**smapes_2, **{\"N-BEATS (M4-trained)\": nbeats_m4_smapes}}\n",
    "\n",
    "elapsed_times_3 = {\n",
    "    **elapsed_times_2,\n",
    "    **{\"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time},\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_3, smapes_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc609d5",
   "metadata": {},
   "source": [
    "### Conclusions so far\n",
    "Although it's not the absolute best in terms of accuracy, our N-BEATS model trained on `m4` reaches competitive accuracies. This is quite remarkable because this model has *not* been trained on *any* of the `air` series we've asked it to forecast! The forecasting step with N-BEATS is more than 1000x faster than the fit-predict step we needed with ARIMA, and also faster than with linear regression.\n",
    "\n",
    "Just for the fun, we can also inspect manually how this model does on another series -- for example, the monthly milk production series available in `darts.datasets`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74f643d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01173f28287d44618247baf6db3e5980",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Month'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from darts.datasets import MonthlyMilkDataset\n",
    "\n",
    "series = MonthlyMilkDataset().load().astype(np.float32)\n",
    "train, val = series[:-24], series[-24:]\n",
    "\n",
    "scaler = Scaler(scaler=MaxAbsScaler())\n",
    "train = scaler.fit_transform(train)\n",
    "val = scaler.transform(val)\n",
    "series = scaler.transform(series)\n",
    "pred = nbeats_model_m4.predict(series=train, n=24)\n",
    "\n",
    "series.plot(label=\"actual\")\n",
    "pred.plot(label=\"0-shot forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b779dd8e",
   "metadata": {},
   "source": [
    "### Try training other global models on `m4` and applying on airline passengers\n",
    "Let's now try to train other global models on the M4 dataset in order to see if we can get similar results. Below, we will train some `SKLearnModel`s on the full `m4` dataset. This can be quite slow. To have faster training we could use e.g., `random.choices(m4_train, k=5000)` instead of `m4_train` to limit the size of the training set. We could also specify some small enough value for `max_samples_per_ts` in order to limit the number of training samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f25d6af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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DBg3Sk08+qbfeekuPPfZYtWvfvn276xTVhTp06KA777xT0rm3UY8ZM0aLFi265IW8Z8+eVWJionJzc/X44497fJbLhfsMCwvT9OnTNWHCBA0bNkyJiYnavHmz3njjDT333HNq2rSp63F//vOflZ6ero0bN7re6tyzZ08NGzZMTzzxhHJyctSxY0e99tprOnz4sBYsWOB23Oeee0533XWX4uLiNH78eB09elQvvfSS+vXrp/79+7vmPf7441q7dq169eqlX/7yl2rWrJlWrVqltWvX6uc///llT2sB9c6H74ACrHfh26gvpapP4p03b57p3r27CQsLM40aNTI333yz+a//+i9z/Phx15yKigqTnp5uWrdubcLCwkyfPn3Mnj17PD6Jt7K3UX/55ZcmISHBREREmObNm5tx48aZ//u///N4W/Do0aNNeHi4R33n39Z8Kdu3bzfDhw83bdu2NSEhIaZly5bmgQceMNu2bXObp///NupLufBt1KWlpaZZs2amV69el3xMbGys6datm1sPLv4k3otd7m3UF/bV27dRV2ef582bN89cd911Jjg42HTo0MH88Y9/dHu7/IU9ufDnasy5T96dNGmSiYqKMiEhIaZHjx7m/fffr7S2zZs3m7vuusuEhoaaFi1amAkTJrjekn6hrVu3mvvvv99ERUWZoKAg07lzZ/Pcc895fMI04A8cxly09gsAAODnuAYGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGCdqz7AOJ1OHTp0yOPGdqgc/fIevaoe+uU9elU99Mt7NvXqqg8wAADAPgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKwT6MuDl5aW6vnnn9enn36q/Px8xcbG6rHHHtMtt9yizMxMPfvsswoODnbNX7ZsmaKionxYMQAA8Ac+DTAVFRVq06aNFixYoJYtW2rdunWaOHGiMjMzJUndu3fX7NmzfVkiAADwQz49hRQWFqZx48YpKipKAQEBSkxMVFBQkI4cOeLLsgAAgJ/z6QrMxbKysnTmzBlFR0dr//792r17t+699141bdpUDz30kIYOHVrp40pLS1VaWuo2FhgY6Hb6qSpOp9PtOy6NfnmPXlUP/fIevaoe+uU9f+hVQIB3aysOY4yp41q8UlxcrLS0NN19990aP368jh07JofDoaioKH355ZeaNGmSHn/8cd17770ej507d67mz5/vNjZs2DAlJyfXV/kAAKAWxMbGejXPLwJMeXm5Jk2apIiICE2fPl0Oh8NjzuLFi7V//349++yzHtt+6ApMdna2oqOjvU59tSUyMvKyc/Ly8uqhEu/5sl+2oVfVQ7+8R6+qh355zx965e1xfX4Kyel0aurUqXI4HHr66acrDS+S5HA4VFXWCg4O9iqsXEpAQEC9/7Dy8/MvO8df/7L5ol+2olfVQ7+8R6+qh355z4Ze+by6GTNm6PTp05o5c6YCA/+dp/75z38qNzdXkvT1119r6dKl6t27t6/KBAAAfsSnKzAnTpzQypUrFRISooSEBNf4yy+/rK1bt2ratGkqKipSy5YtNWrUKCUmJvqwWgAA4C98GmBat26tbdu2VbqtW7dumjhxYj1XBAAAbODzU0gAAADVRYABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWCfR1Abg0h8Nx2TnGmHqoBAAA/8EKDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwjk8DTGlpqdLT0zVgwADFxcUpJSVFu3btcm1fvHixEhISFB8fr1mzZvGJswAAQJKPbyVQUVGhNm3aaMGCBWrZsqXWrVuniRMnKjMzU9u3b9eyZcu0ePFihYaGasKECWrXrp0GDx7sy5IBAIAf8OkKTFhYmMaNG6eoqCgFBAQoMTFRQUFBOnLkiNasWaMhQ4bo2muvVfPmzfXwww9rzZo1viwXAAD4Cb+6mWNWVpbOnDmj6OhoHTp0SImJia5tHTt21IEDByp9XGlpqUpLS93GAgMDFRwcfNljOp1Ot+/1KSIiolb2U5+1+7JftqFX1UO/vEevqod+ec8fehUQ4N3ait8EmOLiYk2dOlUpKSmKiIhQYWGhwsPDXdvDw8NVVFRU6WMXLVqk+fPnu40NGzZMycnJXh8/Ozu7ZoX/ABde7/NDHDlypFb2Ux2+6Jet6FX10C/v0avqoV/e82WvYmNjvZrnFwGmvLxcU6ZMUXR0tMaNGydJatiwoQoKClxzCgoKFBYWVunjx4wZo5EjR7qNVWcFJjs7W9HR0V6nvtoSGRlZK/vJy8urlf14w5f9sg29qh765T16VT30y3s29crnAcbpdGrq1KlyOBx6+umn5XA4JJ1LYPv371dcXJwk6cCBA+rQoUOl+wgODvYqrFxKQEBAvf+w8vPza2U/vniR+aJftqJX1UO/vEevqod+ec+GXvm8uhkzZuj06dOaOXOmAgP/naeSkpL07rvv6ujRozp9+rQyMjKUlJTkw0oBAIC/8OkKzIkTJ7Ry5UqFhIQoISHBNf7yyy/rnnvu0dChQzV69Gg5nU4NHjxYP/7xj31YLQAA8Bc+DTCtW7fWtm3bqtw+ZswYjRkzph4rAgAANvD5KSQAAIDqIsAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDqBvi7gSuVwOHxdAgAAVyxWYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYh7tRXwFq687Xxpha2Q8AAHWNFRgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsI5PA8zy5cs1cuRI9ezZU3PnznWNb9u2TT169FCvXr1cXzt27PBhpQAAwJ/49F5IzZs31/jx4/X+++97bPvRj36klStX1n9RAADA7/k0wPTp00eS9PHHH/uyDAAAYBm/vRv1N998o/vuu08RERFKSkpSamqqGjRoUOnc0tJSlZaWuo0FBgYqODj4ssdxOp1u32tLREREre6vPnjTg7rq15WIXlUP/fIevaoe+uU9f+hVQIB3V7c4jDGmjmu5rBkzZqhZs2ZKS0uTJJ06dUr5+flq27atDh8+rClTpmjQoEF6+OGHK3383LlzNX/+fLexYcOGKTk5uc5rBwAAtSc2NtareX4ZYC72wQcfaOnSpVq4cGGl23/oCkx2draio6O9Tn3eiIyMrLV91Ze8vLzLzqmrfl2J6FX10C/v0avqoV/e84deeXtcvz2FdKHLPZng4GCvwsrljlGbP6z8/Pxa21d9qc7zr+1+XcnoVfXQL+/Rq+qhX96zoVc+ra68vFwlJSVyOp2qqKhQSUmJKioqtG3bNp08eVKSlJWVpQULFqh3796+LBUAAPgRn67ALFiwwO3alYULF2ratGnKy8vT1KlTdfbsWTVt2lRJSUlVXv8CAACuPjUKMD/+8Y913XXX6fe//73b+F/+8hcdPXpUzz//vFf7SUtLq/K6FwILAACoSo0CzPHjx9WsWTOP8U8//VRfffXVDy4KAADgUqoVYFatWuX679zcXLc/FxcX6/DhwwoKCqq96gAAACpRrQCTnp4uh8Mhh8OhY8eO6ZlnnnHbboxRp06darVAAACAi1X7FJIxRg6HQxd/fExISIhiYmI0adKkWisOAACgMtUKMJ999pkkqUePHrr55pur/GA5AACAulSji3jnzJmj8PDw2q4FAADAKzUKMN27d9eRI0f07rvv6rvvvvM4nTRu3LhaKQ4AAKAyNQowf//73zVjxgyP4HIeAQYAANSlGgWYhQsXclvyK5DD4bjsnIqKinqoBACAS6tRgDl9+rQiIiI0f/58xcbGqkGDBrVdFwAAQJVqdDPH2267TY0bN1bHjh0JLwAAoN7VaAUmISFBzz33nJ544gn1799fjRo1ctv+H//xH7VSHAAAQGVqFGDOfyLvhg0btGHDBrdtDodDW7durZXiAAAAKlOjACOpyncgAQAA1LUaBZj33nuvtusAAADwWo0CTOvWrWu7DgAAAK/V+BqYqjgcDj311FM1LggAAOByahRgVq1aVemHnp2/UzUBBgAA1KUaBZhu3bq5BZj8/Hzt379fDodDXbt2ra3aAAAAKlWjADNv3jyPscOHDys1NVW9evX6wUUBAABcSo0+ibcyMTEx6ty5s5YuXVpbuwQAAKhUja+BuZDT6VRWVpZ27Nih0NDQWikMAACgKj/ok3gvZozhNgLwijd3vubDEgEAVam1T+Jt2rSpevTooYkTJ/7gogAAAC6lRgHms88+q+06AAAAvFbjFRhJKikp0cGDByVJ7du3V0hISK0UBQAAcCk1DjALFy7UokWLVFJSIkkKCQnR2LFjlZKSUlu1AQAAVKpGb6P++9//rldffVXFxcUyxsgYo+LiYs2ePVuZmZm1XSMAAICbGq3AvP3225KkPn36KDExUZL0wQcfaNOmTVq6dKkGDhxYexUCAABcpEYB5vDhw2rTpo1eeOEF11hCQoIGDRqkQ4cO1VpxAAAAlanRKaQGDRqopKRE5eXlrrHy8nKVlJSoQYMGtVYcAABAZWq0AtO5c2ft2rVL48ePV9++fSVJGzduVG5urm699dZaLRAAAOBiNQowP/vZzzRp0iTt2bNHe/bskfTvD7YbNWpU7VUHAABQiRqdQoqLi1N6erpatWrlehdSVFSUpk+fzt2oAQBAnavWCszx48e1fft2xcTEKCkpSUlJScrNzZUkZWdnKysrS8ePH1ebNm3qpFgAAACpmiswixcv1jPPPON28W6TJk3UpEkTFRcX65lnntHixYtru0YAAAA31VqB2bZtm8LDw9W1a1ePbbfffrsaNWp0VdwnyZs7KV/N6A8AoK5VawUmJydHUVFRVW5v1aqVcnJyfnBRAAAAl1KtANOgQQOdOHFCTqfTY1tFRYWOHz+uwMAfdH9IAACAy6pWgImNjVVhYaFmz57tsW3OnDkqKChQbGxsrRUHAABQmWotlyQkJOiLL77Q3/72N23ZskXdunWTw+HQzp079a9//UsOh0P33XdfXdUKAAAgqZoBJjk5WWvXrtXevXu1b98+7du3z7XNGKPrrrtOycnJtV4kAADAhap1Cik4OFhz5sxRYmKiAgICXB9iFxAQoP79+2v27NkKCgqqq1oBAAAk1eBWAo0aNdKzzz6rKVOmKCsrS8YYtWvXThEREXVRHwAAgIcav2UoIiJCN954Y23WAgAA4JUa3QsJAADAlwgwAADAOgQYAABgHQIMAACwDgEGAABYhwCDaomMjHR9dzgclX4BAFDXCDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDo+DTDLly/XyJEj1bNnT82dO9dtW2ZmppKSkhQXF6f09HSVlZX5qEoAAOBvfBpgmjdvrvHjxys+Pt5tfP/+/frDH/6gF154QatXr9Y333yjv/71rz6qEgAA+BufBpg+ffooLi5OjRo1cht///33FR8fr5tuukkRERFKTU3V6tWrfVQlAADwN4G+LqAyBw8e1O233+76c8eOHXXy5EkVFhaqYcOGHvNLS0tVWlrqNhYYGKjg4ODLHsvpdLp990ZERITXc6804eHhbt/rUnV+Jv6oJq+tqxn98h69qh765T1/6FVAgHdrK34ZYIqKitz+B3k+MFQVYBYtWqT58+e7jQ0bNkzJycleHzM7O9vrubt27fJ67pVqy5YtdX6MI0eO1Pkx6kN1XlugX9VBr6qHfnnPl72KjY31ap5fBpiwsDAVFBS4/pyfny9JlYYXSRozZoxGjhzpNladFZjs7GxFR0d7nfoiIyO9mnclCg8P15YtW3TnnXe6/YzqQl5eXp3uv67V5LV1NaNf3qNX1UO/vGdTr/wywLRv31779+93/fnAgQOKioqqMsAEBwd7FVYuJSAgwOsf1vlAdTUrKCio8z74+18eb1XntQX6VR30qnrol/ds6JVPqysvL1dJSYmcTqcqKipUUlKiiooK9e/fXx9++KG++uor5efna+HChRowYIAvSwUAAH7EpyswCxYscLt2ZeHChZo2bZoGDhyoiRMn6rHHHlNBQYHi4+M1duxYH1YKAAD8iU8DTFpamtLS0irdNnDgQA0cOLCeKwIAADbw7xNcAAAAlSDAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBlc8h8Nx2a+6OFZkZKQkKTIysk6OBQBXMwIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsE+roA4Ieoz7s7+9uxjDH1UAkA+CdWYAAAgHUIMAAAwDoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADwK9ERkbK4XBc8gsACDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYJ1AXxdwKePHj9eePXvUoEEDSVK3bt308ssv+7gqAADga34dYCTpd7/7nZKSknxdBgAA8CN+H2C8UVpaqtLSUrexwMBABQcHX/axTqfT7bs3IiIiqlfgFSQ8PNzte13y5mdSWz+LujhWVb2qrWNV5zVrg/PPx5vX1pX23KurJr+3rmb0y3v+0KuAAO+ubnEYY0wd11Jj48eP18GDByVJnTt31sSJE9WpUyePeXPnztX8+fPdxoYNG6bk5OR6qRMAANSO2NhYr+b5dYDZs2eP2rdvr4CAAC1dulRvvfWWli9f7vEvtB+6ApOdna3o6GivU19kZKT3T+IKEx4eri1btujOO+9UQUGBr8vxa1X1Ki8v77KP9eY15s1+bHL+76I3r60r7blXV01+b13N6Jf3/KFX3h7Xr08hdenSxfXfo0eP1nvvvafdu3frjjvucJsXHBzsVVi5lICAAK+blp+f/4OOdSUoKCigD166uFfevM686e2V+ovYm9fWlfrcq6s6v7dAv6rDhl75d3UX8fdmAgCA+uG3ieDs2bP65JNPVFpaqrKyMmVkZOjMmTNuqzIAAODq5LenkMrLy/WXv/xFR44cUWBgoDp37qxZs2Zd1e8AAgAA5/htgGnSpIlef/11X5cBAAD8kN+eQgIAAKgKAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDp++zkwwJXK4XDU2378+F6tAPCDsAIDAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDrcjRq4ynFX6/pBn4HaxQoMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUCfV0AAP/ncDhqZT/GmFrZz5Vajzf7qa2a69OV+rzgW6zAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACsQ4ABAADWIcAAAADrEGAAAIB1CDAAAMA6BBgAAGAdAgwAALAOAQZAvXE4HFV+RUZG+lU91fmq75rP9yoyMtLn9dQWf+tzbfHH15A3qnpt+VPNBBgAAGAdAgwAALAOAQYAAFiHAAMAAKxDgAEAANYhwAAAAOsQYAAAgHUIMAAAwDoEGAAAYB2/DjC5ubn69a9/rXvuuUcPPvigPv30U1+XBAAA/IBfB5j//u//VrNmzbR+/Xr9+te/1hNPPKG8vDxflwUAAHzMbwNMYWGhNm3apLS0NIWGhiouLk4dOnTQP/7xD1+XBgAAfCzQ1wVUJSsrSw0bNlSrVq1cYx07dtTBgwc95paWlqq0tNRtLDAwUMHBwZc9jtPpdPvujYiICK/nXmnCw8PdvqNq/tArb17X/vJ69od+2eJyvarO77P6UFuvsZo+r5r8nq8t3j53f/mZna/Dm7+HdVVzQIB3ayt+G2CKioo8GhgeHl7pKaRFixZp/vz5bmPjxo1TWlraZY8TEBCg2NjYatV29uzZas2/Ep08edLXJVjD33vlb69nf++XP7GlV75+jdXk93xt8fVzr67zvbLhteW3ASYsLEwFBQVuYwUFBWrYsKHH3DFjxmjkyJFuY96svgAAADv57TUwbdu2VWFhoXJyclxjBw4cUPv27T3mBgcHKyIiwu2LAAMAwJXLbwNMw4YNFRcXp7lz56q4uFibN2/W/v37FRcX5+vSAACAjzmMMcbXRVQlNzdX06ZN0+eff65WrVpp8uTJ6tmzp6/LAgAAPubXAQYAAKAyfnsKCQAAoCoEGAAAYB0CDAAAsA4BBgAAWIcAAwAArHNVB5jc3Fz9+te/1j333KMHH3xQn376qa9L8hulpaVKT0/XgAEDFBcXp5SUFO3atcu1ffHixUpISFB8fLxmzZol3sx2zq5du9SjRw/99a9/dY3Rq8q99tprGjBggHr37q0RI0a4Pnmbfnn617/+pdTUVMXFxenHP/6xVq5cKencvWheeukl9enTR/369VNGRoZvC/WB5cuXa+TIkerZs6fmzp3rti0zM1NJSUmKi4tTenq6ysrKXNuOHj2q1NRU3X333Ro5cqT27t1b36XXu6p69dFHH7leX/3799cf/vAHlZeXu7b7ba/MVWzy5MkmPT3dFBUVmU2bNpn4+Hjz/fff+7osv1BYWGjmzZtnTpw4YSoqKsz7779v4uPjTUFBgdm8ebNJSkoy2dnZ5ttvvzXJyclmxYoVvi7Z5yoqKszo0aPNqFGjzPz5840xhl5VYenSpSYtLc2cOHHCOJ1Os3fvXlNSUkK/qpCcnGzmzZtnKioqzFdffWV69eplDh48aN5++20zfPhwc/r0aXPkyBHTv39/s3XrVl+XW682btxoNm3aZKZMmWLmzJnjGt+3b5/p06eP2bNnjzl79qx55JFHzOzZs13bf/azn5k5c+aY4uJis2zZMjNo0CBTVlbmi6dQb6rq1dq1a82WLVtMUVGR+e6778zYsWPNggULXNv9tVdX7QpMYWGhNm3apLS0NIWGhiouLk4dOnTQP/7xD1+X5hfCwsI0btw4RUVFKSAgQImJiQoKCtKRI0e0Zs0aDRkyRNdee62aN2+uhx9+WGvWrPF1yT737rvvqkuXLm43jaNXnioqKrRw4UL97ne/U1RUlBwOhzp16qTg4GD6VYUTJ04oMTFRAQEBuv766xUTE6PDhw9rzZo1evjhh9W0aVO1bdtWgwcP1urVq31dbr3q06eP4uLi1KhRI7fx999/X/Hx8brpppsUERGh1NRUV28OHz6sQ4cOacyYMQoJCdHQoUPldDq1c+dOHzyD+lNVr/r376877rhDoaGhatKkiZKSkrR7925J/t2rqzbAZGVlqWHDhmrVqpVrrGPHjjp48KAPq/JfWVlZOnPmjKKjo3Xo0CF16tTJta1jx446cOCAD6vzve+//15LlizxuAM6vfKUk5Oj4uJirV+/Xv369dODDz6oFStWSKJfVXnooYe0du1alZeXa8+ePfrmm29088036+DBgx794nfYOZX15uTJkyosLNShQ4fUtm1bt3vm8Vr7tx07drjuO+jPvfLbu1HXtaKiIoWHh7uNhYeHKy8vz0cV+a/i4mJNnTpVKSkpioiIUGFhoVvvwsPDVVRU5MMKfW/27NkaPny4x79s6JWnnJwc5efnKysrS++9956ys7P1yCOPKCYmhn5V4a677tK0adO0cOFCSdLUqVPVvHlzj99j4eHhKiws9FWZfuXi3kREREg693fy4teZxGvtvA0bNujTTz/VkiVLJHn+DpP8p1dXbYAJCwtzXTR4XkFBgRo2bOijivxTeXm5pkyZoujoaI0bN07SuRttXti7goIChYWF+apEn/v666/15ZdfavLkyR7b6JWnkJAQSdK4ceMUGhqqTp06qV+/fvr444/pVyXy8vL0m9/8RlOnTlXfvn118OBB/epXv1LHjh09fo/xO+zfLu5Nfn6+pHN/Jy9+nUm81iRp27ZtmjlzpmbNmqWmTZtK8vwdJvlPr67aU0ht27ZVYWGhcnJyXGMHDhxwLZvh3Dscpk6dKofDoaeffloOh0OSFBsbq/3797vmHThwQB06dPBVmT63fft2HTlyRElJSUpMTNS6dev0t7/9Tenp6fSqEu3atVNQUJDr9SSJ19YlHD16VKGhoUpISFCDBg3UqVMn3XLLLfr888/Vvn17j37xO+ycynoTFRWlhg0bKjY2VtnZ2SotLXXbfjW/1vbs2aMpU6bo+eef14033uga9+deXbUBpmHDhoqLi9PcuXNVXFyszZs3a//+/YqLi/N1aX5jxowZOn36tGbOnKnAwH8v1iUlJendd9/V0aNHdfr0aWVkZCgpKcmHlfrW+Ws4MjIylJGRod69e2vYsGF67LHH6FUlwsLCdO+992rBggUqLS3VoUOHtG7dOt199930qxLt2rVTcXGxNm3aJGOMDh48qJ07d6pjx466//779frrrys3N1fZ2dlauXKlBgwY4OuS61V5eblKSkrkdDpVUVGhkpISVVRUqH///vrwww/11VdfKT8/XwsXLnT1JiYmRjExMVq8eLFKS0v17rvvyuFwqGvXrr59MnWsql7t379fEydO1NSpU3Xbbbe5Pcafe3VV3406NzdX06ZN0+eff65WrVpp8uTJ6tmzp6/L8gsnTpzQwIEDFRISooCAf+fcl19+Wd26ddOiRYv0xhtvyOl0avDgwXr00Ufd/kV9NXv66ad17bXX6uc//7kk0atKnD17Vs8884y2bt2qa665RikpKXrwwQcl0a/KbNmyRa+88oqOHj2qxo0ba+jQoUpJSZHT6dQf//hHZWZmKigoSKNHj9bDDz/s63Lr1dy5czV//ny3sWnTpmngwIHKzMzU7NmzVVBQoPj4eP32t791XYyanZ2tadOm6V//+pfatWunadOm6brrrvPFU6g3VfVq+/btWr16tev0riR169ZNL7/8siT/7dVVHWAAAICdrtpTSAAAwF4EGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAda7amzkC8I0LPw00KChIq1atUrNmzVzbR40apS+//FKS1KVLFy1evNjt8ZmZmUpPT5ckBQQE6L333lNUVJTbnIs/Dj0oKEgtW7bU7bffrtTUVLVu3VqSNH78eG3fvr3SOl988UX16dOnxs8TQN1iBQaAz5SVlendd991/XnPnj2u8FKVzMxM1387nU6tWrWqyrnXXHONunTpotatW+vYsWNasWKFxo4d63F33aCgIHXp0sXtq3HjxjV8VgDqAyswAGrdRx99pAULFujQoUMqKytTixYtdMMNN+iJJ55wmxcYGKh33nlHY8aMUWBgoJYuXeoaLy8v99jvsWPHtGPHDknSjTfeqC+//FKrV6923XfqYvfcc4+efvppSdKsWbP0+uuvKycnR5999pnb6krz5s09VnoA+DdWYADUqtzcXD3++OPavXu3IiIi1LZtW+Xl5WndunXKz893mxsfH69Tp05pw4YNOn36tNavX69WrVqpS5cule571apVMsaoWbNmevLJJyWdu9Hczp076/ppAfAzrMAAqFUnT55UWVmZwsPDtXz5coWGhsoYoy+//FJNmjRxm/vQQw/pf/7nf7R06VJlZWWprKxMQ4cO1ZYtWzz2a4zRmjVrJEn9+/fXddddp06dOmnfvn3KzMxU165dPR7z0UcfKSUlRWfPntWRI0ckSS1atFCPHj3c5p04ccLjuplt27b9kDYAqGMEGAC1qn379vrRj36kY8eOqV+/foqOjlbHjh0VHx+vm266yW3u9ddfr1tuuUW7du3SgQMHFBISoiFDhlQaYD7//HMdO3ZMkpSUlOT6PmvWLK1fv16PP/64QkND3R7z/fff6/vvv1dgYKDatGmjnj17KjU1VeHh4W7zgoKCdN1119VmGwDUMQIMgFoVEhKiN954Q6tXr9YXX3yhgwcPas2aNVq9erVmzpzpMf+hhx7Srl27VFBQoIEDB+qaa66pdL8XXqyblpYmSaqoqJAkFRQU6MMPP3QFm/MeeOAB1zUwl8I1MIB9uAYGQK3Kz8/XoUOH9NBDD2n69OnKyMjQHXfcIUmVvmX53nvvVfPmzSWdCzOVKSws1IYNG9yOkZ+fr6KiItfYpd6NBODKwwoMgFqVm5ur1NRUNW7cWC1btlRZWZnr+pNOnTopJyfHbf75dyKVlZVVufqyYcMGV1hZunSpOnTo4Nq2ZMkSvfTSS9q2bZtOnjzp8Zkw3jh16pRSUlLcxkaMGKF+/fpVe18A6gcrMABqVWRkpAYOHKimTZvq+PHj+uabbxQTE6MJEyZo8ODBlT4mPDy8yvAi/fuzX9q2besWXiSpb9++ki7/mTCXUlZWpj179rh9nTp1qkb7AlA/HMYY4+siAAAAqoMVGAAAYB0CDAAAsA4BBgAAWIcAAwAArEOAAQAA1iHAAAAA6xBgAACAdQgwAADAOgQYAABgHQIMAACwDgEGAABYhwADAACs8/8Aw6vl0msCKfwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "\n",
    "lr_model_m4 = LinearRegressionModel(lags=30, output_chunk_length=1)\n",
    "lr_model_m4.fit(m4_train)\n",
    "\n",
    "tic = time.time()\n",
    "preds = lr_model_m4.predict(n=HORIZON, series=air_train)\n",
    "lr_time_transfer = time.time() - tic\n",
    "\n",
    "lr_smapes_transfer = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4e1a8fc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Some label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.097573 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 7650\n",
      "[LightGBM] [Info] Number of data points in the train set: 8063744, number of used features: 30\n",
      "[LightGBM] [Info] Start training from score 0.722222\n",
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(42)\n",
    "\n",
    "lgbm_model_m4 = LightGBMModel(lags=30, output_chunk_length=1, objective=\"mape\")\n",
    "lgbm_model_m4.fit(m4_train)\n",
    "\n",
    "tic = time.time()\n",
    "preds = lgbm_model_m4.predict(n=HORIZON, series=air_train)\n",
    "lgbm_time_transfer = time.time() - tic\n",
    "\n",
    "lgbm_smapes_transfer = eval_forecasts(preds, air_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7fc449",
   "metadata": {},
   "source": [
    "Finally, let's plot these new results as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae5b3355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_4 = {\n",
    "    **smapes_3,\n",
    "    **{\n",
    "        \"Linear Reg (M4-trained)\": lr_smapes_transfer,\n",
    "        \"LGBM (M4-trained)\": lgbm_smapes_transfer,\n",
    "    },\n",
    "}\n",
    "\n",
    "elapsed_times_4 = {\n",
    "    **elapsed_times_3,\n",
    "    **{\n",
    "        \"Linear Reg (M4-trained)\": lr_time_transfer,\n",
    "        \"LGBM (M4-trained)\": lgbm_time_transfer,\n",
    "    },\n",
    "}\n",
    "\n",
    "plot_models(elapsed_times_4, smapes_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef9f3be",
   "metadata": {},
   "source": [
    "## Part 4 and recap: Use the same model on M3 dataset\n",
    "OK, now, were we lucky with the airline passengers dataset? Let's see by repeating the entire process on a new dataset :) You will see that it actually requires very few lines of code. As a new dataset, we will use `m3`, which contains about 1,400 monthly series from the M3 competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fd30aebf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building M3 TimeSeries...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba15df754c884c41bdd3a08ceb985fe0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There are 1399 monthly series in the M3 dataset\n",
      "splitting train/test...\n",
      "scaling...\n",
      "done. There are 1399 series, with average training length 100.30092923516797\n"
     ]
    }
   ],
   "source": [
    "m3_train, m3_test = load_m3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab72e092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6b4cc93cb2e43d98b5cd9e2820bdab7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive1_smapes_m3, naive1_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, NaiveSeasonal, K=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f3ac97e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9c8c99221c244d081985a071c189b2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "naive12_smapes_m3, naive12_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, NaiveSeasonal, K=12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "251dd9c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8476785ec004819ad0738f4ecc95949",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ets_smapes_m3, ets_time_m3 = eval_local_model(m3_train, m3_test, ExponentialSmoothing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "93469d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81975e241d064fbbaf6d2c76361185cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_smapes_m3, theta_time_m3 = eval_local_model(m3_train, m3_test, Theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b9edb800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb000a21bbcb4602bb36715c60d81f5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")  # ARIMA generates lots of warnings\n",
    "\n",
    "# Note: using q=1 here generates errors for some series, so we use q=0\n",
    "arima_smapes_m3, arima_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, ARIMA, p=12, d=1, q=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "76e56c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a864e5f3d64d48f1aabea68cab435655",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1399 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kf_smapes_m3, kf_time_m3 = eval_local_model(\n",
    "    m3_train, m3_test, KalmanForecaster, dim_x=12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb2eff2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_smapes_m3, lr_time_m3 = eval_global_model(\n",
    "    m3_train, m3_test, LinearRegressionModel, lags=30, output_chunk_length=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "39a81a9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Some label values are < 1 in absolute value. MAPE is unstable with such values, so LightGBM rounds them to 1.0 when calculating MAPE.\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004819 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 8925\n",
      "[LightGBM] [Info] Number of data points in the train set: 91356, number of used features: 35\n",
      "[LightGBM] [Info] Start training from score 0.771293\n",
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbm_smapes_m3, lgbm_time_m3 = eval_global_model(\n",
    "    m3_train, m3_test, LightGBMModel, lags=35, output_chunk_length=1, objective=\"mape\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "355f8190",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a884d556172348d79d14cb2d8ecce3f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained N-BEATS\n",
    "\n",
    "start_time = time.time()\n",
    "preds = nbeats_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "nbeats_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "nbeats_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3658e9ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained linear regression model\n",
    "\n",
    "start_time = time.time()\n",
    "preds = lr_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "lr_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "lr_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3b984116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing sMAPEs...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get forecasts with our pre-trained LightGBM model\n",
    "\n",
    "start_time = time.time()\n",
    "preds = lgbm_model_m4.predict(series=m3_train, n=HORIZON)  # get forecasts\n",
    "lgbm_m4_elapsed_time_m3 = time.time() - start_time\n",
    "\n",
    "lgbm_m4_smapes_m3 = eval_forecasts(preds, m3_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cc711d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smapes_m3 = {\n",
    "    \"naive-last\": naive1_smapes_m3,\n",
    "    \"naive-seasonal\": naive12_smapes_m3,\n",
    "    \"Exponential Smoothing\": ets_smapes_m3,\n",
    "    \"ARIMA\": arima_smapes_m3,\n",
    "    \"Theta\": theta_smapes_m3,\n",
    "    \"Kalman filter\": kf_smapes_m3,\n",
    "    \"Linear Regression\": lr_smapes_m3,\n",
    "    \"LGBM\": lgbm_smapes_m3,\n",
    "    \"N-BEATS (M4-trained)\": nbeats_m4_smapes_m3,\n",
    "    \"Linear Reg (M4-trained)\": lr_m4_smapes_m3,\n",
    "    \"LGBM (M4-trained)\": lgbm_m4_smapes_m3,\n",
    "}\n",
    "\n",
    "times_m3 = {\n",
    "    \"naive-last\": naive1_time_m3,\n",
    "    \"naive-seasonal\": naive12_time_m3,\n",
    "    \"Exponential Smoothing\": ets_time_m3,\n",
    "    \"ARIMA\": arima_time_m3,\n",
    "    \"Theta\": theta_time_m3,\n",
    "    \"Kalman filter\": kf_time_m3,\n",
    "    \"Linear Regression\": lr_time_m3,\n",
    "    \"LGBM\": lgbm_time_m3,\n",
    "    \"N-BEATS (M4-trained)\": nbeats_m4_elapsed_time_m3,\n",
    "    \"Linear Reg (M4-trained)\": lr_m4_elapsed_time_m3,\n",
    "    \"LGBM (M4-trained)\": lgbm_m4_elapsed_time_m3,\n",
    "}\n",
    "\n",
    "plot_models(times_m3, smapes_m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ec6ec8",
   "metadata": {},
   "source": [
    "Here too, the pre-trained N-BEATS model obtains reasonable accuracy, although not as good as the most accurate models. Note also that Exponential Smoothing and Kalman Filter now perform much better than when we used them on the air passengers series. ARIMA performs best but is about 1000x slower than N-BEATS, which didn't require any training and takes about 15 ms per time series to produce its forecasts. Recall that this N-BEATS model has *never* been trained on *any* of the series we're asking it to forecast.\n",
    "\n",
    "## Conclusions\n",
    "Transfer learning and meta learning is definitely an interesting phenomenon that is at the moment under-explored in time series forecasting. When does it succeed? When does it fail? Can fine tuning help? When should it be used? Many of these questions still have to be explored but we hope to have shown that doing so is quite easy with Darts models.\n",
    "\n",
    "Now, which method is best for your case? As always, it depends. If you're dealing mostly with isolated series that have a sufficient history, classical methods such as ARIMA will get you a long way. Even on larger datasets, if compute power is not too much an issue, they can represent interesting out-of-the-box options. On the other hand if you're dealing with larger number of series, or series of higher dimensionalities, ML methods and global models will often be the way to go. They can capture patterns across wide ranges of different time series, and are in general faster to run. Don't under-estimate linear regression based models in this category! If you have reasons to believe you need to capture more complex patterns, or if inference speed is *really* important for you, give deep learning methods a shot. N-BEATS has proved its worth for meta-learning [1], but this can potentially work with other models too.\n",
    "\n",
    "[1] Oreshkin et al., \"Meta-learning framework with applications to zero-shot time-series forecasting\", 2020, https://arxiv.org/abs/2002.02887"
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