sktime alternatives

3 tools to consider instead of sktime, shown against it.

	sktime	Darts	Prophet	PyTorch Forecasting
Vendor	sktime community (open source)	Unit8 SA	Meta (Facebook Core Data Science)	PyTorch Forecasting community (open source)
Pricing model	Open source + paid options	Open source + paid options	Open source + paid options	Open source + paid options
Free tier	Yes	Yes	Yes	Yes
Deployment	Self-hosted	Self-hosted	Self-hosted	Self-hosted
Open source	Yes (BSD-3-Clause)	Yes (Apache-2.0)	Yes (MIT)	Yes (MIT)
Best for	Data scientists who want rigorous, leakage-free forecast evaluation using scikit-learn-style pipelines.	Teams that want to benchmark many forecasting model types against the same dataset through one consistent interface.	Analysts who need a fast, interpretable forecast on a single time series with clear seasonality and holidays.	ML engineers forecasting large panels of related series (many products, stores or sensors) who want deep-learning models with covariates.
Pricing	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.	Free and open source with no commercial tier. Pricing has not been verified yet — see the vendor's site.
Features	scikit-learn-compatible estimator interface for forecasting Time-series classification, regression and clustering in the same library Pipeline composition with detrending/deseasonalizing transformers Rolling-window and temporal cross-validation utilities Wrappers around statistical, ML and deep-learning forecasters Hierarchical and panel-data forecasting support Consistent API for model comparison and ensembling	Unified fit/predict API across 30+ forecasting models Classical, gradient-boosting, deep-learning and foundation models in one library Probabilistic forecasting with prediction intervals Covariate (external variable) support Backtesting and model comparison utilities Anomaly detection via scorers and detectors Ensemble and conformal prediction wrappers	Additive model of trend, seasonality and holiday effects Automatic handling of missing data and outliers Custom holiday and event regressors Uncertainty intervals on forecasts Python and R implementations Automatic changepoint detection for trend shifts Minimal parameter tuning required for a first forecast	Temporal Fusion Transformer, N-BEATS, DeepAR-style and other neural forecasters TimeSeriesDataSet abstraction for panel/multi-series data Static and time-varying covariate support Built on PyTorch Lightning for training loops and logging Attention-based interpretability for Temporal Fusion Transformer Quantile/probabilistic forecasting output GPU-accelerated training for large panels

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