unit8co/darts

Health v2 · maintenance only

Adoption not part of the score

9505 stars · 1030 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Darts is a Python library for user-friendly time series forecasting and anomaly detection. It provides a unified scikit-learn-like fit/predict API across models ranging from classical methods like ARIMA to deep neural networks, with support for probabilistic forecasting, backtesting, and multivariate series.

Use cases

• forecast future values of a time series in python
• detect anomalies in time series data
• train forecasting models on multiple time series
• compare ARIMA vs neural network forecasting models
• produce probabilistic forecasts with confidence intervals
• backtest a forecasting model on historical data
• use covariates like holidays or weather in time series forecasting

When to choose

• you want one consistent API for many forecasting models, from ARIMA to deep learning
• you need anomaly detection built on top of forecasting or filtering models
• you want probabilistic forecasting and model backtesting out of the box
• you work with univariate or multivariate time series in pandas

When to avoid

• you need lightweight, dependency-free statistical forecasting only
• your use case is general tabular ML regression rather than time series
• you require real-time streaming anomaly detection at very low latency

Facets

library · maturity active

machine-learning data-science nlp time-series machine-learning data-science deep-learning python forecasting anomaly-detection time-series probabilistic-forecasting arima deep-learning scikit-learn-style

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