Darts: User-Friendly Modern Machine Learning for Time Series
Published in
Journal of machine learning research(2021)External Links:
TL
TL;DR
The emphasis of the library is on offering modern machine learning functionalities, such as supporting multidimensional series, meta-learning on multiple series, training on large datasets, incorporating external data, ensembling models, and providing a rich support for probabilistic forecasting.
Abstract
We present Darts, a Python machine learning library for time series, with a focus on forecasting. Darts offers a variety of models, from classics such as ARIMA to state-of-the-art deep neural networks. The emphasis of the library is on offering modern machine learning functionalities, such as supporting multidimensional series, meta-learning on multiple series, training on large datasets, incorporating external data, ensembling models, and providing a rich support for probabilistic forecasting. At the same time, great care goes into the API design to make it user-friendly and easy to use. For instance, all models can be used using fit()/predict(), similar to scikit-learn.
References23 items
1
Merlion: A Machine Learning Library for Time Series
2
Array programming with NumPy
3
Meta-learning framework with applications to zero-shot time-series forecasting
4
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
5