Time series forecasting pipelines
- examples.simple.time_series_forecasting.ts_pipelines.ts_ets_pipeline()[source]
Return pipeline with the following structure:
Where cut - cut part of dataset and ets - exponential smoothing
- examples.simple.time_series_forecasting.ts_pipelines.ts_ets_ridge_pipeline()[source]
Return pipeline with the following structure:
Where cut - cut part of dataset, ets - exponential smoothing
- examples.simple.time_series_forecasting.ts_pipelines.ts_glm_pipeline()[source]
Return pipeline with the following structure:
Where glm - Generalized linear model
- examples.simple.time_series_forecasting.ts_pipelines.ts_glm_ridge_pipeline()[source]
Return pipeline with the following structure:
Where glm - Generalized linear model
- examples.simple.time_series_forecasting.ts_pipelines.ts_polyfit_pipeline(degree)[source]
Return pipeline with the following structure:
Where polyfit - Polynomial interpolation
- examples.simple.time_series_forecasting.ts_pipelines.ts_polyfit_ridge_pipeline(degree)[source]
Return pipeline with the following structure:
Where polyfit - Polynomial interpolation
- examples.simple.time_series_forecasting.ts_pipelines.ts_complex_ridge_pipeline()[source]
Return pipeline with the following structure:
- examples.simple.time_series_forecasting.ts_pipelines.ts_complex_ridge_smoothing_pipeline()[source]
Pipeline looking like this
Where smoothing - rolling mean
- examples.simple.time_series_forecasting.ts_pipelines.ts_complex_dtreg_pipeline(first_node='lagged')[source]
Return pipeline with the following structure:
Where dtreg = tree regressor, rfr - random forest regressor
- examples.simple.time_series_forecasting.ts_pipelines.ts_multiple_ets_pipeline()[source]
Return pipeline with the following structure:
Where ets - exponential_smoothing
- examples.simple.time_series_forecasting.ts_pipelines.ts_ar_pipeline()[source]
Return pipeline with the following structure:
Where ar - auto regression
- examples.simple.time_series_forecasting.ts_pipelines.ts_arima_pipeline()[source]
Return pipeline with the following structure:
- examples.simple.time_series_forecasting.ts_pipelines.ts_stl_arima_pipeline()[source]
Return pipeline with the following structure:
- examples.simple.time_series_forecasting.ts_pipelines.ts_locf_ridge_pipeline()[source]
Pipeline with naive LOCF (last observation carried forward) model and lagged features