This package has a series of forecasters for univariate time series data.
It also has a function to combine forecasts into an ensemble.
It mostly uses sktime which does a good job of porting R::forecast to Python.
However it has a few extensions
- more datasets
- SGT forecaster
- TFP STS forecaster (really slow)
- Fourier extrapolator
- MicroPrediction fast and slow
- Diffencing and RF multi output
- Period on period growth
- Greykite/Silverkite
- Online Bandit approach
- ensembling with worst predictors removed (trimmed)
- https://www.sktime.org/en/stable/get_started.html
- https://github.com/alan-turing-institute/sktime
- https://alkaline-ml.com/pmdarima/
- https://facebook.github.io/prophet/docs/quick_start.html
- https://pydlm.github.io/
- https://github.com/linkedin/greykite
- https://github.com/microprediction/timemachines
- https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/univariate-k_001.html
- https://docs.pymc.io/en/v3/pymc-examples/examples/time_series/Air_passengers-Prophet_with_Bayesian_workflow.html
- https://github.com/deepcharles/ruptures
- http://www.xavierdupre.fr/app/mlinsights/helpsphinx/notebooks/piecewise_linear_regression.html
- https://www.unofficialgoogledatascience.com/2017/04/our-quest-for-robust-time-series.html
- https://www.statsmodels.org/dev/generated/statsmodels.tsa.forecasting.stl.STLForecast.html
- https://juanitorduz.github.io/gp_ts_pymc3/
- https://towardsdatascience.com/multi-step-time-series-forecasting-with-arima-lightgbm-and-prophet-cc9e3f95dfb0
- multivariate or X variables