Today we are glad to announce these exciting features! π
* Improved StatsForecast class
* Selection of best-performing models
* New documentation
* Plotting functionalities
Stay tuned for an exciting polemic comparison tomorrow!
#reinventforecasting
* Improved StatsForecast class
* Selection of best-performing models
* New documentation
* Plotting functionalities
Stay tuned for an exciting polemic comparison tomorrow!
#reinventforecasting
StatsForecast class:
With the new StatsForecast class, you can fit many models for many time series in less than 15 lines of code
You can include @dask_dev, @raydistributed, and #spark by changing just the backend parameter
With the new StatsForecast class, you can fit many models for many time series in less than 15 lines of code
You can include @dask_dev, @raydistributed, and #spark by changing just the backend parameter
Models: StatsForecast 27 models.
They can be included and imported in a few lines of code.
They can be included and imported in a few lines of code.
Documentation π
Because you asked for it: new documentation with practical use cases.
πΒ End-to-End Walkthrough: Model training, evaluation, and selection for multiple time series
πΒ Anomaly Detection: detect anomalies for time series using in-sample prediction intervals
Because you asked for it: new documentation with practical use cases.
πΒ End-to-End Walkthrough: Model training, evaluation, and selection for multiple time series
πΒ Anomaly Detection: detect anomalies for time series using in-sample prediction intervals
More use cases!
π©βπ¬ Cross Validation: robust modelβs performance evaluation.
βοΈ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL.
π Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.
π©βπ¬ Cross Validation: robust modelβs performance evaluation.
βοΈ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL.
π Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.
More use cases!
π Intermittent Demand: forecast series with very few non-zero observations.
π‘οΈ Exogenous Regressors: like weather or prices
π Intermittent Demand: forecast series with very few non-zero observations.
π‘οΈ Exogenous Regressors: like weather or prices
Plotting!π
The plot method allows you to do EDA, compare models or series, and even conveniently detect anomalies.
Engines supported: @plotlygraphs and @matplotlib
The plot method allows you to do EDA, compare models or series, and even conveniently detect anomalies.
Engines supported: @plotlygraphs and @matplotlib
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