Downloaddarts 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. 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).
Features
- A large collection of forecasting models; from statistical models (such as ARIMA) to deep learning models (such as N-BEATS)
- TimeSeries can be multivariate - i.e., contain multiple time-varying dimensions instead of a single scalar value
- All machine learning based models (incl. all neural networks) support being trained on multiple (potentially multivariate) series
- 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
- Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts
- In addition to time-dependent data, TimeSeries can also contain static data for each dimension, which can be exploited by some models
Project Samples
