| I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes * Statistical models (ETS, (V)ARIMA(X), etc) * ML models (sklearn models, LGBM, etc) * Many recent deep learning models (N-BEATS, TFT, etc) * Seamlessly works on multi-dimensional series * Models can be trained on multiple series * Several models support taking in external data (covariates), known either in the past only, or also in the future * Many models offer rich support for probabilistic forecasts * Model evaluation is easy: Darts has many metrics, offers backtest etc * Deep learning scales to large datasets, using GPUs, TPUs, etc * You can do reconciliation of forecasts at different hierarchical levels * There's even now an explainability module for some of the models - showing you what matters for computing the forecasts * (coming soon): an anomaly detection module :) * (also, it even include FB Prophet if you really want to use it) Warning: I'm probably biased because I'm Darts creator. [1] https://github.com/unit8co/darts |