Time Series Made Easy in Python

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darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, and combine the predictions of several models and external regressors. Darts supports both univariate and multivariate time series and models, and the neural networks can be trained on multiple time series.


High Level Introductions


We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper).

Once your environment is set up you can install darts using pip:

pip install darts

For more detailed install instructions you can refer to our installation guide at the end of this page.

Example Usage

Create a TimeSeries object from a Pandas DataFrame, and split it in train/validation series:

import pandas as pd
from darts import TimeSeries

df = pd.read_csv('AirPassengers.csv', delimiter=",")
series = TimeSeries.from_dataframe(df, 'Month', '#Passengers')
train, val = series.split_after(pd.Timestamp('19580101'))

The dataset used in this example can be downloaded from this link.

Fit an exponential smoothing model, and make a prediction over the validation series’ duration:

from darts.models import ExponentialSmoothing

model = ExponentialSmoothing()
prediction = model.predict(len(val))


import matplotlib.pyplot as plt

prediction.plot(label='forecast', lw=2)
darts forecast example

We invite you to go over the example and tutorial notebooks in the examples directory.


Currently, the library contains the following features:

Forecasting Models:

  • Exponential smoothing,

  • ARIMA & auto-ARIMA,

  • Facebook Prophet,

  • Theta method,

  • FFT (Fast Fourier Transform),

  • Recurrent neural networks (vanilla RNNs, GRU, and LSTM variants),

  • Temporal convolutional network.

  • Transformer


Data processing: Tools to easily apply (and revert) common transformations on time series data (scaling, boxcox, …)

Metrics: A variety of metrics for evaluating time series’ goodness of fit; from R2-scores to Mean Absolute Scaled Error.

Backtesting: Utilities for simulating historical forecasts, using moving time windows.

Regressive Models: Possibility to predict a time series from several other time series (e.g., external regressors), using arbitrary regressive models

Multivariate Support: Tools to create, manipulate and forecast multivariate time series.


The development is ongoing, and there are many new features that we want to add. We welcome pull requests and issues on GitHub.

Before working on a contribution (a new feature or a fix) make sure you can’t find anything related in issues. If there is no on-going effort on what you plan to do then we recommend to do the following:

  1. Create an issue, describe how you would attempt to solve it, and if possible wait for a discussion.

  2. Fork the repository.

  3. Clone the forked repository locally.

  4. Create a clean Python env and install requirements with pip: pip install -r requirements/dev-all.txt

  5. Create a new branch:

    • Branch off from the develop branch.

    • Prefix the branch with the type of update you are making:

      • feature/

      • fix/

      • refactor/

    • Work on your update

  6. Check that your code passes all the tests and design new unit tests if needed: ./gradlew unitTest_all.

  7. Verify your tests coverage by running ./gradlew coverageTest

    • Additionally you can generate an xml report and use VSCode Coverage gutter to identify untested lines with ./coverage.sh xml

  8. If your contribution introduces a significant change, add it to CHANGELOG.md under the “Unreleased” section.

  9. Create a pull request from your new branch to the develop branch.

Contact Us

If what you want to tell us is not a suitable github issue, feel free to send us an email at darts@unit8.co for darts related matters or info@unit8.co for any other inquiries.

Installation Guide

Some of the models depend on fbprophet and torch, which have non-Python dependencies. A Conda environment is thus recommended because it will handle all of those in one go.

The following steps assume running inside a conda environment. If that’s not possible, first follow the official instructions to install fbprophet and torch, then skip to Install darts

To create a conda environment for Python 3.7 (after installing conda):

conda create --name <env-name> python=3.7

Don’t forget to activate your virtual environment

conda activate <env-name>
conda install -c conda-forge -c pytorch pip fbprophet pytorch
conda install -c conda-forge -c pytorch pip fbprophet pytorch cpuonly

Install Darts with all available models: pip install darts.

As some models have relatively heavy (or non-Python) dependencies, we also maintain the u8darts package, which provides the following alternate lighter install options:

  • Install core only (without neural networks, Prophet or AutoARIMA): pip install u8darts

  • Install core + neural networks (PyTorch): pip install 'u8darts[torch]'

  • Install core + Facebook Prophet: pip install 'u8darts[fbprophet]'

  • Install core + AutoARIMA: pip install 'u8darts[pmdarima]'

If the conda setup is causing too many problems, we also provide a Docker image with everything set up for you and ready-to-use Python notebooks with demo examples. To run the example notebooks without installing our libraries natively on your machine, you can use our Docker image:

./gradlew docker && ./gradlew dockerRun

Then copy and paste the URL provided by the docker container into your browser to access Jupyter notebook.

For this setup to work you need to have a Docker service installed. You can get it at Docker website.

The gradle setup works best when used in a python environment, but the only requirement is to have pip installed for Python 3+

To run all tests at once just run

./gradlew test_all

alternatively you can run

./gradlew unitTest_all # to run only unittests
./gradlew coverageTest # to run coverage
./gradlew lint         # to run linter

To run the tests for specific flavours of the library, replace _all with _core, _fbprophet, _pmdarima or _torch.

To build documantation locally just run

./gradlew buildDocs

After that docs will be available in ./docs/build/html directory. You can just open ./docs/build/html/index.html using your favourite browser.

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