Darts

All models use consistent fit() and predict() methods, enabling easy model swapping and reducing the learning curve, as emphasized in the README's comparison to scikit-learn.

Includes over 50 models from classics like ARIMA to deep learning (N-BEATS, TCN) and wrappers for StatsForecast and NeuralForecast, providing a one-stop shop for diverse forecasting needs.

Handles multiple time-varying dimensions and offers probabilistic forecasting with confidence intervals, crucial for risk-aware decisions in complex datasets.

The darts.ad module simplifies anomaly detection with scorers, detectors, and PyOD integration, allowing seamless combination with forecasting models for end-to-end analysis.

Machine learning models can be trained on multiple time series datasets, scaling to large volumes and improving generalization, as highlighted in the features list.