Darts
The Python library that unifies time-series forecasting, from classic models to deep learning.
Darts, built by Unit8, exposes a single fit/predict API across dozens of forecasting models: ARIMA, Exponential Smoothing, Prophet and Theta, but also lag-based regression (LightGBM, XGBoost) and deep networks such as N-BEATS, TFT, TiDE or TSMixer. Its TimeSeries object handles multivariate series, past and future covariates, probabilistic forecasting, backtesting and anomaly detection. It's our choice when several model families need a fair comparison on the same series before the best one goes to production.
What Darts brings to your project.
Typical use cases: Sales and demand forecasting, inventory planning, energy consumption, capacity planning.
- 01
Unified fit/predict API: ARIMA, Prophet, boosting and deep learning are interchangeable.
- 02
Global models trained on thousands of series with past and future covariates.
- 03
Probabilistic forecasting: quantiles and intervals to quantify uncertainty.
- 04
Built-in backtesting and historical forecasts, plus an anomaly-detection module.
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Entrust your project
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