Timeseries ========== .. contents:: :local: Datasets ++++++++ .. autosignature:: mlinsights.timeseries.datasets.artificial_data Experimentation +++++++++++++++ .. autosignature:: mlinsights.timeseries.patterns.find_ts_group_pattern Manipulation ++++++++++++ .. autosignature:: mlinsights.timeseries.agg.aggregate_timeseries Plotting ++++++++ .. autosignature:: mlinsights.timeseries.plotting.plot_week_timeseries Prediction ++++++++++ The following function builds a regular dataset from a timeseries so that it can be used by machine learning models. .. autosignature:: mlinsights.timeseries.selection.build_ts_X_y The first class defined the template for all timeseries estimators. It deals with a timeseries ine one dimension and additional features. .. autosignature:: mlinsights.timeseries.base.BaseTimeSeries the first predictor is a dummy one: it uses the current value to predict the future. .. autosignature:: mlinsights.timeseries.dummies.DummyTimeSeriesRegressor The first regressor is an auto-regressor. It can be estimated with any regressor implemented in :epkg:`scikit-learn`. .. autosignature:: mlinsights.timeseries.ar.ARTimeSeriesRegressor The library implements one scoring function which compares the prediction to what a dummy predictor would do by using the previous day as a prediction. .. autosignature:: mlinsights.timeseries.metrics.ts_mape