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Forecasting statplus7/13/2023 ![]() ![]() □ Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills. ❄️ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL. □□ Cross Validation: robust model’s performance evaluation. □ Anomaly Detection: detect anomalies for time series using in-sample prediction intervals. □ End to End Walkthrough: Model training, evaluation and selection for multiple time series Missing something? Please open an issue or write us in Examples and Guides Fit 10 benchmark models on 1,000,000 series in under 5 min.Replace FB-Prophet in two lines of code and gain speed and accuracy.Compiled to high performance machine code through numba.Inclusion of exogenous variables and prediction intervals for ARIMA. ![]() Support for exogenous Variables and static covariates.Probabilistic Forecasting and Confidence Intervals.Out-of-the-box compatibility with Spark, Dask, and Ray. ![]() Fastest and most accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL and Theta in Python. ![]() StatsForecast includes an extensive battery of models that can efficiently fit millions of time series. So we created a library that can be used to forecast in production environments or as benchmarks. Why?Ĭurrent Python alternatives for statistical models are slow, inaccurate and don’t scale well.
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