{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NeuralForecast Model Integration\n", "\n", "This notebook demonstrates how to use [NeuralForecast](https://nixtlaverse.nixtla.io/neuralforecast/docs/) models in Darts through the `NeuralForecastModel` wrapper.\n", "\n", "`NeuralForecastModel` converts any of the 30+ NeuralForecast base models into a Darts `TorchForecastingModel`, giving access to the full Darts torch ecosystem: covariates, probabilistic forecasting, optimized historical forecasts/backtesting, model saving/loading, and more.\n", "\n", "**Supported models include**: TiDE, PatchTST, KAN, NBEATSx, TSMixer, TimeXer, iTransformer, FEDformer, Autoformer, Informer, and many others.\n", "\n", "**Installation**: `NeuralForecastModel` requires the `neuralforecast` package (not included in `darts[all]`). For example you can install the package from PyPI with:\n", "\n", "```bash\n", "pip install \"neuralforecast>=3.0.0\"\n", "```\n", "\n", "Other installation options can be found in the [NeuralForeacst installation guide](https://nixtlaverse.nixtla.io/neuralforecast/docs/getting-started/installation.html).\n", "\n", "For more details, see the [Darts documentation](https://unit8co.github.io/darts/) and the original [NeuralForecast documentation](https://nixtlaverse.nixtla.io/neuralforecast/docs/).\n", "\n", "The follwing topics are covered in this notebook:\n", "* [Quick Start: Univariate Forecasting](#1.-Quick-Start:-Univariate-Forecasting)\n", "* [Forecasting Using Covariates](#2.-Forecasting-Using-Covariates)\n", "* [Historical Forecasts & Backtesting](#3.-Historical-Forecasts-&-Backtesting)\n", "* [Multivariate Forecasting](#4.-Multivariate-Forecasting)\n", "* [Multiple Series Forecasting](#5.-Multiple-Series-Forecasting)\n", "* [Probabilistic Forecasting](#6.-Probabilistic-Forecasting)\n", "* [Comparison: Darts TiDE vs NeuralForecast TiDE](#7.-Comparison:-Darts-TiDE-vs-NeuralForecast-TiDE)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import warnings\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from darts import set_option\n", "from darts.datasets import AirPassengersDataset\n", "from darts.metrics import mae, mape, rmse\n", "from darts.models import NeuralForecastModel, TiDEModel\n", "from darts.utils.likelihood_models import QuantileRegression\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "logging.disable(logging.CRITICAL)\n", "\n", "set_option(\"plotting.use_darts_style\", True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Quick Start: Univariate Forecasting\n", "\n", "Let's start with a simple univariate (single column) forecast example using the AirPassengers dataset." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "series = AirPassengersDataset().load().astype(\"float32\")\n", "\n", "# the models input and output windows\n", "input_chunk_length = 24\n", "output_chunk_length = 12\n", "\n", "# extract a training and validation set for illustration\n", "train, val = series[:-output_chunk_length], series[-output_chunk_length:]\n", "\n", "train.plot(label=\"train\")\n", "val.plot(label=\"val\", title=\"Train and Test Split\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `NeuralForecastModel` is straightforward: set `model` to any NeuralForecast base model name (as a string) or class (see all available models [here](https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html)), and pass model-specific architectural parameters via `model_kwargs`. Everything else (training, covariates, likelihoods) works exactly like any other Darts `TorchForecastingModel`.\n", "\n", "
\n", " **Info**: Darts will take care of automatically setting non-architectural parameters for you. These parameters **will be ignored** in `model_kwargs`:\n", "
    \n", "
  1. **Input and output parameters**: `input_size`, `h`, and `n_series` These are automatically set to match the `input_chunk_length`, `output_chunk_length`, and number of target series (components), respectively.\n", "
  2. **Covariate parameters**: `futr_exog_list`, `hist_exog_list`, and `stat_exog_list`. These are inferred directly from the input time series passed to `fit()`.\n", "
  3. **Training and PyTorch (Lightning)-related setup**: `loss`, `learning_rate`, `max_steps`, etc. are all handled by Darts. You can specify these parameters directly. You can find all parameters in the `kwargs` parameter description [here](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel).\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fbf840d9bff745519f8cb07f3c9d1318", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define parameters shared by all Darts' models\n", "darts_kwargs = dict(\n", " input_chunk_length=input_chunk_length, # the model's input window\n", " output_chunk_length=output_chunk_length, # the model's output window\n", " use_reversible_instance_norm=True, # reversible instance normalization against distribution shift\n", " n_epochs=50, # train the model for 50 epochs\n", " random_state=42,\n", " pl_trainer_kwargs=dict(\n", " accelerator=\"auto\"\n", " ), # configure the PyTorch Lightning Trainer for advanced techniques\n", ")\n", "\n", "model = NeuralForecastModel(\n", " model=\"MLP\",\n", " model_kwargs=dict(\n", " num_layers=2, hidden_size=1024\n", " ), # set NeuralForecast model hyperparameters\n", " **darts_kwargs,\n", ")\n", "\n", "# train and predict; training only on a single series allows us to call `predict()` without having to pass a `series`.\n", "# the forecast will start after the end of that training series\n", "model.fit(train)\n", "pred = model.predict(n=output_chunk_length)\n", "\n", "# plot\n", "series[-3 * output_chunk_length :].plot(label=\"actual\")\n", "pred.plot(label=\"MLP\", title=f\"NeuralForecast's MLP\\nMAE: {mae(val, pred):.2f}\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Forecasting Using Covariates\n", "\n", "Many NeuralForecast models support covariates. The wrapper automatically detects which covariate types are supported by the chosen base model and wires them into the Darts pipeline. There is no need to specify `futr_exog_list`, `hist_exog_list`, and `stat_exog_list`. These are inferred directly from the input time series passed to `NeuralForecastModel.fit()`.\n", "\n", "Since we don't have any future covariates at hand, let's generate some from calendar attributes of the AirPassengers series and see whether including them has an impact on model performance.\n", "\n", "
\n", " **Tip**: For simplicity, you can also let the model generate these features for you with the `add_encoders` parameter. More info in the [quickstart](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Encoders:-using-covariates-for-free). \n", "
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from darts.dataprocessing.transformers import Scaler\n", "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", "\n", "# generate feature: number of days per month, and apply a MinMax scaler to scale all values into the range (0,1)\n", "future_covariates = datetime_attribute_timeseries(series, attribute=\"days_in_month\")\n", "future_covariates = Scaler().fit_transform(future_covariates)\n", "\n", "# let's also add a cyclic encoding (sin + cosine wave) of the value the month\n", "future_covariates = future_covariates.add_datetime_attribute(\n", " \"month\", cyclic=True\n", ").astype(\"float32\")\n", "\n", "# finally, display everything\n", "future_covariates.plot(title=\"Future Covariates\");" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "408651050784467188dac0595ff74c97", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the MLP model supports all types or covariates, let's get a fresh model instance\n", "model = model.untrained_model()\n", "\n", "# fit and predict\n", "model.fit(train, future_covariates=future_covariates)\n", "forecast_cov = model.predict(n=output_chunk_length)\n", "\n", "# plot\n", "series[-3 * output_chunk_length :].plot(label=\"actual\")\n", "forecast_cov.plot(\n", " label=\"MLP\", title=f\"MLP with Future Covariates\\nMAE: {mae(val, forecast_cov):.2f}\"\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, it looks like the model performs better with the covariates and reduced the MAE from 19.52 to 15.53!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Historical Forecasts & Backtesting\n", "\n", "Since `NeuralForecastModel` is a Darts `TorchForecastingModel`, it has access to all other Darts functionality. For **more robust evaluation**, we should make use of Darts' **optimized historical forecast & backtesting** routine (similar to NeuralForecast's cross validation)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3652abea73164d06af0a4ed91640b241", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# get a fresh model instance and pre-train it on everything except the last 36 points\n", "model = model.untrained_model()\n", "model.fit(series[:-36], future_covariates=future_covariates)\n", "\n", "# generate forecasts sequentially over the last 36 points\n", "historical_forecasts = model.historical_forecasts(\n", " series=series,\n", " future_covariates=future_covariates,\n", " start=-36,\n", " forecast_horizon=output_chunk_length,\n", " stride=output_chunk_length, # move 12 steps ahead after each forecast\n", " retrain=False, # no re-training, only apply pre-trained model\n", " verbose=True,\n", " last_points_only=False,\n", ")\n", "\n", "ax = series.plot(label=\"actual\")\n", "for idx, forecast in enumerate(historical_forecasts):\n", " ax = forecast.plot(label=f\"forecast: {idx}\", ax=ax)\n", "ax.set_title(\"MLP\\nHistorical Forecasts\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now we can compute any number of metrics on the historical forecasts (backtesting).\n", "\n", "
\n", " **Tip**: Backtesting can also generate the historical forecasts directly without us having to pre-compute them. It supports the same parameters as `historical_forecasts()`.\n", "
" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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forecast: 048.12893750.33307612.857846
forecast: 125.45626629.1530175.779883
forecast: 237.90531542.0286257.934020
\n", "
" ], "text/plain": [ " MAE RMSE MAPE\n", "forecast: 0 48.128937 50.333076 12.857846\n", "forecast: 1 25.456266 29.153017 5.779883\n", "forecast: 2 37.905315 42.028625 7.934020" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metrics = model.backtest(\n", " series=series,\n", " historical_forecasts=historical_forecasts,\n", " last_points_only=False,\n", " metric=[mae, rmse, mape],\n", " reduction=None, # do not aggregate individual forecast scores\n", ")\n", "\n", "pd.DataFrame(\n", " metrics,\n", " columns=[\"MAE\", \"RMSE\", \"MAPE\"],\n", " index=[f\"forecast: {i}\" for i in range(len(historical_forecasts))],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Multivariate Forecasting\n", "\n", "Multivariate (multiple target columns) forecasting is supported by all base models. Simply pass a multivariate time series as `series` to `fit()` and `predict()`.\n", "\n", "
\n", " **Info**: For **univariate base models**, multivariate forecasting is achieved by folding the target components into the batch dimension and repeating the covariates for each target component accordingly. This translates to **global training** and forecasting **on multiple univariate series**.\n", "
" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# generate a multivariate series\n", "series_multivar = series.stack(series * -1 + series.max(axis=0))\n", "series_multivar.plot(title=\"Multivariate Series\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the time index of our series hasn't changed, we can re-use the same future covariates." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "188a994189fc4714980c54be08e40892", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# let's use TSMixerx, a multivariate base model\n", "model = NeuralForecastModel(model=\"TSMixerx\", **darts_kwargs)\n", "\n", "# fit and predict\n", "model.fit(\n", " series=series_multivar[:-output_chunk_length],\n", " future_covariates=future_covariates,\n", ")\n", "pred = model.predict(n=output_chunk_length)\n", "\n", "# plot\n", "series_multivar[-3 * output_chunk_length :].plot(label=\"actual\")\n", "pred.plot(\n", " label=\"TSMixerx\",\n", " title=f\"NeuralForecast's TSMixerx\\nMultivariate Series - MAE: {mae(series_multivar, pred):.2f}\",\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Multiple Series Forecasting\n", "\n", "Global training and forecasting on multiple time series is supported by all base models. Simply pass a sequence of uni- or multivariate time series as ``series`` to `fit()` and `predict()`.\n", "\n", "
\n", " **Info**: When fitting a model on **multiple series**, we must always **pass some input series and covariates** to `predict()`. The models forecast after the end of each series. These series **do not** have to be the ones that the model was trained on!\n", "
" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d21ed138496c407b97f9858b9c570e7d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# split the multivariate series into two (multiple) univariate series\n", "series_multiple = [series_multivar[\"#Passengers\"], series_multivar[\"#Passengers_1\"]]\n", "series_multiple_train = [s[:-output_chunk_length] for s in series_multiple]\n", "\n", "# since the covariates apply to both series, we can simply repeat them\n", "future_covariates_multiple = [future_covariates] * len(series_multiple)\n", "\n", "# get a fresh model instance\n", "model = model.untrained_model()\n", "\n", "# fit and predict\n", "model.fit(\n", " series=series_multiple_train,\n", " future_covariates=future_covariates_multiple,\n", ")\n", "forecasts = model.predict(\n", " n=output_chunk_length,\n", " series=series_multiple_train,\n", " future_covariates=future_covariates_multiple,\n", ")\n", "\n", "# plot\n", "ax = series_multivar[-3 * output_chunk_length :].plot(label=\"actual\")\n", "for idx, forecast in enumerate(forecasts):\n", " ax = forecast.plot(label=f\"forecast: series {idx}\", ax=ax)\n", "ax.set_title(\n", " f\"NeuralForecast's TSMixerx\\nMultiple Series - MAE: \"\n", " f\"{mae(series_multiple, forecasts, series_reduction=np.mean):.2f}\"\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Probabilistic Forecasting\n", "\n", "Probabilistic forecasting is supported by all base models thanks to our **Darts likelihood models**.\n", "\n", "Let's use **Quantile Regression** with a **KAN** (Kolmogorov-Arnold Network) model.\n", "\n", "
\n", " **Info**: Darts offers many other likelihood models such as Gaussian, Poisson, Laplace and more. You can find a complete list [here](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.torch.html).\n", "
" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "782a2892d8484d6d91aa432dde71ca86", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# KAN model that forecasts expected quantiles [0.1, 0.5, 0.9]\n", "model = NeuralForecastModel(\n", " model=\"KAN\",\n", " likelihood=QuantileRegression(quantiles=[0.1, 0.5, 0.9]),\n", " **darts_kwargs,\n", ")\n", "\n", "# fit and predict\n", "model.fit(train)\n", "pred_prob = model.predict(\n", " n=output_chunk_length,\n", " predict_likelihood_parameters=True, # predict quantiles directly\n", ")\n", "\n", "# plot\n", "series[-3 * output_chunk_length :].plot(label=\"actual\")\n", "pred_prob.plot(title=\"KAN with Quantile Regression\\nDirect Quantile Prediction\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also generate **sampled predictions**. This is especially useful when performing auto-regression (when horizon > output_chunk_length), as it can model the increasing uncertainty with longer horizons. Essentially, it performs a **Monte Carlo Simulation** for different sample paths." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b8b0e3ada8374c92afd97585a8b11593", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Predicting: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred_prob = model.predict(\n", " n=5 * output_chunk_length,\n", " num_samples=200, # sampled prediction\n", ")\n", "\n", "# plot\n", "series[-3 * output_chunk_length :].plot(label=\"actual\")\n", "pred_prob.plot(\n", " label=\"KAN (probabilistic)\",\n", " title=\"KAN with Quantile Regression\\nAutoregressive Prediciton with Monte Carlo Samples\",\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Comparison: Darts TiDE vs NeuralForecast TiDE\n", "\n", "Since Darts has its own native TiDE implementation, let's compare it side by side with the NeuralForecast version. This helps validate the integration and shows that NeuralForecast models can achieve comparable results.\n", "\n", "We pass `model_kwargs` with similar hyperparaters as Darts' `TiDEModel` (you can find the default parameters [in the API documentation](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel))." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8ba50c0ab07d4b77a38b383118776c5c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Darts' TiDE model\n", "model_darts = TiDEModel(**darts_kwargs)\n", "\n", "# NeuralForecast's TiDE model\n", "model_nf = NeuralForecastModel(\n", " model=\"TiDE\",\n", " model_kwargs={\n", " \"hidden_size\": 128,\n", " \"decoder_output_dim\": 16,\n", " \"dropout\": 0.1,\n", " \"layernorm\": False,\n", " },\n", " **darts_kwargs,\n", ")\n", "\n", "ax = series[-3 * output_chunk_length :].plot(label=\"actual\")\n", "\n", "for model_i, name in [(model_darts, \"Darts TiDE\"), (model_nf, \"NF TiDE\")]:\n", " model_i.fit(train)\n", " forecast = model_i.predict(n=output_chunk_length)\n", " ax = forecast.plot(label=f\"{name}\\nMAE: {mae(val, forecast):.2f}\", ax=ax)\n", "\n", "ax.set_title(\"Darts TiDE vs NeuralForecast TiDE\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two architectures are very similar but not identical. That's why we see a small difference in model performance. The difference should become negligible when performing robust model training.\n", "\n", "You can read about more advanced techniques in our [TorchForecastingModel user guide](https://unit8co.github.io/darts/userguide/torch_forecasting_models.html#advanced-functionnalities)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "The `NeuralForecastModel` wrapper makes it easy to:\n", "\n", "- **Access 30+ neural forecasting architectures** through a unified interface.\n", "- **Use all Darts torch features**: Uni- & multivariate forecasting, multiple series forecasting, probabilistic forecasting, covariates (past, future, static), optimized backtesting, model saving/loading, encoders, and more.\n", "- **Quickly experiment** with different architectures by simply changing the `model` parameter.\n", "- **Advanced modelling techniques** are described in our [TorchForecastingModel user guide](https://unit8co.github.io/darts/userguide/torch_forecasting_models.html#advanced-functionnalities).\n", "\n", "### Limitations\n", "\n", "- **Recurrent models** (GRU, LSTM, RNN, DeepAR) are not supported — Darts provides native implementations.\n", "- The core `NeuralForecast` class and automatic models (e.g. `AutoInformer`) are not supported.\n", "- `neuralforecast>=3.0.0` must be installed separately (see the [NeuralForeacst installation guide](https://nixtlaverse.nixtla.io/neuralforecast/docs/getting-started/installation.html)).\n", "\n", "For more information, see the [NeuralForecastModel API reference](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nf_model.html)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": 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