u8darts 0.18.0

A python library for easy manipulation and forecasting of time series.

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, 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.

Documentation

• Quickstart
• API Reference
• Examples

High Level Introductions

• Introductory Blog Post
• Introduction to Darts at PyData Global 2021

Articles on Selected Topics

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

Quick Install

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.

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

# 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()

Features

• 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.
• 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.
• Regression Models: Possibility to predict a time series from lagged versions of itself and of some external covariate series, using arbitrary regression models (e.g. scikit-learn models).
• Multiple series training: All machine learning based models (incl.\ all neural networks) support being trained on multiple series.
• Past and Future Covariates support: Some models support past-observed and/or future-known covariate time series as inputs for producing forecasts.
• Multivariate Support: Tools to create, manipulate and forecast multivariate time series.
• Probabilistic Support: TimeSeries objects can (optionally) represent stochastic time series; this can for instance be used to get confidence intervals, and several models support different flavours of probabilistic forecasting.
• 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.

Forecasting Models

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

Model	Univariate	Multivariate	Probabilistic	Multiple-series training	Past-observed covariates support	Future-known covariates support	Reference
ARIMA	✅		✅			✅
VARIMA	✅	✅				✅
AutoARIMA	✅					✅
ExponentialSmoothing	✅		✅
BATS and TBATS	✅		✅				TBATS paper
Theta and FourTheta	✅						Theta & 4 Theta
Prophet	✅		✅			✅	Prophet repo
FFT (Fast Fourier Transform)	✅
KalmanForecaster using the Kalman filter and N4SID for system identification	✅	✅	✅			✅	N4SID paper
RegressionModel; generic wrapper around any sklearn regression model	✅	✅		✅	✅	✅
RandomForest	✅	✅		✅	✅	✅
LinearRegressionModel	✅	✅	✅	✅	✅	✅
LightGBMModel	✅	✅	✅	✅	✅	✅
RNNModel (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version	✅	✅	✅	✅		✅	DeepAR paper
BlockRNNModel (incl. LSTM and GRU)	✅	✅	✅	✅	✅
NBEATSModel	✅	✅	✅	✅	✅		N-BEATS paper
TCNModel	✅	✅	✅	✅	✅		TCN paper, DeepTCN paper, blog post
TransformerModel	✅	✅	✅	✅	✅
TFTModel (Temporal Fusion Transformer)	✅	✅	✅	✅	✅	✅	TFT paper, PyTorch Forecasting
Naive Baselines	✅

Community & Contact

Anyone is welcome to join our Discord server to ask questions, make proposals, discuss use-cases, and more. If you spot a bug or or have a feature request, 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 [e-mail] for darts related matters or [e-mail] for any other inquiries.

Contribute

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.

Citation

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

Darts: User-Friendly Modern Machine Learning for Time Series

Bibtex entry:

@misc{herzen2021darts,
      title={Darts: User-Friendly Modern Machine Learning for Time Series},
      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},
      year={2021},
      eprint={2110.03224},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}