forecastflowml.ForecastFlowML.cross_validate#

ForecastFlowML.cross_validate(df, n_cv_splits=3, max_train_size=None, cv_step_length=None, refit=True, spark=None)[source]#

Time series cross validation predictions

Parameters:

  • df – Dataset to fit.

  • n_cv_splits (int) – Number of cross validation folds.

  • max_train_size (Optional[int]) – Number of max periods to use as training set.

  • cv_step_length (Optional[int]) – Number of periods to put between each cv folds.

  • refit (bool) – Whether to refit model for each training dataset.

  • spark (Optional[pyspark.sql.session.SparkSession]) – Spark session instance. Only provide when df is a pandas DataFrame.

Return type:

DataFrame that contains target and predictions over cross validation folds.