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, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting.

• Quickstart
• User Guide
• API Reference
• Examples

• Introductory Blog Post
• Introduction video (25 minutes)

• Training Models on Multiple Time Series
• Using Past and Future Covariates
• Temporal Convolutional Networks and Forecasting
• Probabilistic Forecasting
• Transfer Learning for Time Series Forecasting

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 details you can refer to our installation instructions.

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

import pandas as pd
from darts import TimeSeries

# Read a pandas DataFrame
df = pd.read_csv('AirPassengers.csv', delimiter=",")

# Create a TimeSeries, specifying the time and value columns
series = TimeSeries.from_dataframe(df, 'Month', '#Passengers')

# Set aside the last 36 months as a validation series
train, val = series[:-36], series[-36:]

Fit an exponential smoothing model, and make a (probabilistic) prediction over the validation series' duration:

from darts.models import ExponentialSmoothing

model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val), num_samples=1000)

Plot the median, 5th and 95th percentiles:

import matplotlib.pyplot as plt

series.plot()
prediction.plot(label='forecast', low_quantile=0.05, high_quantile=0.95)
plt.legend()

• Forecasting Models: A large collection of forecasting models; from statistical models (such as ARIMA) to deep learning models (such as N-BEATS). See table of models below.
• Multivariate Support: TimeSeries can be multivariate - i.e., contain multiple time-varying dimensions instead of a single scalar value. Many models can consume and produce multivariate series.
• Multiple series training: All machine learning based models (incl. all neural networks) support being trained on multiple (potentially multivariate) series. This can scale to large datasets.
• Probabilistic Support: TimeSeries objects can (optionally) represent stochastic time series; this can for instance be used to get confidence intervals, and many models support different flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles).
• Past and Future Covariates support: Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts.
• Static Covariates support: In addition to time-dependent data, TimeSeries can also contain static data for each dimension, which can be exploited by some models.
• Hierarchical Reconciliation: Darts offers transformers to perform reconciliation. These can make the forecasts add up in a way that respects the underlying hierarchy.
• Regression Models: It is possible to plug-in any scikit-learn compatible model to obtain forecasts as functions of lagged values of the target series and covariates.
• Data processing: Tools to easily apply (and revert) common transformations on time series data (scaling, filling missing values, 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.
• PyTorch Lightning Support: All deep learning models are implemented using PyTorch Lightning, supporting among other things custom callbacks, GPUs/TPUs training and custom trainers.
• Filtering Models: Darts offers three filtering models: KalmanFilter, GaussianProcessFilter, and MovingAverage, which allow to filter time series, and in some cases obtain probabilistic inferences of the underlying states/values.
• Datasets The darts.datasets submodule contains some popular time series datasets for rapid experimentation.

Here's a breakdown of the forecasting models currently implemented in Darts. We are constantly working on bringing more models and features.

Anyone is welcome to join our Discord server Gitter room to ask questions, make proposals, discuss use-cases, and more. If you spot a bug or or have suggestions, GitHub issues are also welcome.

If what you want to tell us is not suitable for Discord or Github, feel free to send us an email at [email protected] for darts related matters or [email protected] for any other inquiries.

The development is ongoing, and we welcome suggestions, pull requests and issues on GitHub. All contributors will be acknowledged on the change log page.

Before working on a contribution (a new feature or a fix), check our contribution guidelines.

If you are using Darts in your scientific work, we would appreciate citations to the following JMLR paper.

Darts: User-Friendly Modern Machine Learning for Time Series

Bibtex entry:

@article{JMLR:v23:21-1177,
  author  = {Julien Herzen and Francesco Lässig and Samuele Giuliano Piazzetta and Thomas Neuer and Léo Tafti and Guillaume Raille and Tomas Van Pottelbergh and Marek Pasieka and Andrzej Skrodzki and Nicolas Huguenin and Maxime Dumonal and Jan Kościsz and Dennis Bader and Frédérick Gusset and Mounir Benheddi and Camila Williamson and Michal Kosinski and Matej Petrik and Gaël Grosch},
  title   = {Darts: User-Friendly Modern Machine Learning for Time Series},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {124},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v23/21-1177.html}
}