The open-source tool for time series forecasting and anomaly detection

Large collection of classical and state-of-the-art forecasting models: from statistical models (such as ARIMA) to deep learning models (such as N-BEATS). We are constantly scouting research and bringing the best models to Darts. Some of Darts’ models have extra features not present in the original versions.

Detect and classify anomalies in your series: It’s trivial to apply PyOD, or Darts’ forecasting and Filtering models to obtain fully fledged anomaly detection models.

Multivariate series: Each of the time series can have multiple dimensions, to more easily model complex systems

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

Evaluating forecasts correctly is very important: Darts provides the tools to simulate and evaluate historical forecasts, using moving or expanding time windows.

Understand what your model learned: from feature importances and shap explanations for regression models, to self attention for transformer models.

Data processing: Easily manipulate and/or apply (and potentially revert) common transformations on time series data (window transformations, missing values handling, normalization, etc …)

De-noise your series: Darts provides several filtering methods from Gaussian Process Regressor to Moving Average filters..

All deep learning models are implemented using PyTorch Lightning: supporting among other things custom callbacks, GPUs/TPUs training, checkpointing, weights loading for fine-tuning, etc.