sktime

Why we included this project

Python teams working with time series often end up stitching together separate packages for forecasting, classification, and anomaly detection, each with its own conventions. sktime folds these tasks into one scikit-learn-style API, so the fit/predict workflow you already know carries over to univariate, multivariate, and panel data. It bundles a wide set of estimators, from classical exponential smoothing and ARIMA-family models to interval- and distance-based classifiers, deep learning, and reduction wrappers that recast forecasting as regression, plus tooling for tuning, ensembling, and validation. Because the interfaces mirror sklearn, existing pipelines and validation habits keep working instead of forcing a new framework. If you want one dependable place to explore and ship time series models across many domains rather than assembling one-off scripts, this is a strong pick.