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Darts

Unit8
open source / Overall score: 3.4

Time-series forecasting and anomaly-detection library from Unit8 with one fit/predict interface over many model kinds - baselines, statistical models such as ARIMA, ETS, Theta and TBATS, regression models on scikit-learn, LightGBM, XGBoost and CatBoost, deep models such as N-BEATS, N-HiTS, TFT, TiDE and TSMixer, and pretrained foundation models such as Chronos-2 and TimesFM that forecast without training. It adds backtesting, ensembling, conformal prediction and covariate support.

Openness

5 high confidence
5.0
license
Apache-2.0(OSI)
source
public(github.com/unit8co/darts)
core features withheld
no — Unit8 sells consulting and support engagements, not an edition

Apache-2.0 and built from the public repository. Unit8, which built and maintains it, sells consulting projects and support retainers around Darts, but no feature of the library is held back for paying customers.

Adoption

3 high confidence
3.0

Measured on monthly PyPI downloads of the darts package. Unit8 also publishes the same library as u8darts, with a smaller count that is not added here.

Capability

4 medium confidence
4.0

Darts puts dozens of forecasting models behind one interface, and its foundation models forecast a series the user brings with no training run, as TabPFN predicts on a new table. Choosing among its other models, and tuning them, is still the user's work; it does not search for a model automatically.

  • https://raw.githubusercontent.com/unit8co/darts/HEAD/README.md recorded 2026-09-27

    README: "Darts is a Python library for user-friendly forecasting and anomaly detection on time series"; model table with Baseline, Statistical, Regression, Deep Learning, Ensemble and "Foundation Models ... No training required" (Chronos2Model, TimesFM2p5Model, TimesFM3Model, TiRexModel) sections.

Verified 2026-09-27