gluonts.ext.naive_2 package#

class gluonts.ext.naive_2.Naive2Predictor(prediction_length: int, season_length: int)[source]#

Bases: gluonts.model.predictor.RepresentablePredictor

Naïve 2 forecaster as described in the M4 Competition Guide.

See: http://www.unic.ac.cy/test/wp-content/uploads/sites/2/2018/09/M4-Competitors-Guide.pdf.

The Python analogue implementation to: https://github.com/Mcompetitions/M4-methods/blob/master/Benchmarks%20and%20Evaluation.R#L118

Parameters

  • prediction_length – Number of time points to predict

  • season_length – Length of the seasonality pattern of the input data

predict_item(item: Dict[str, Any]) → gluonts.model.forecast.Forecast[source]#
gluonts.ext.naive_2.naive_2(past_ts_data: numpy.ndarray, prediction_length: int, season_length: int) → numpy.ndarray[source]#

Make seasonality adjusted time series prediction.

For details, see: http://www.unic.ac.cy/test/wp-content/uploads/sites/2/2018/09/M4-Competitors-Guide.pdf

Code based on: https://github.com/Mcompetitions/M4-methods/blob/master/Benchmarks%20and%20Evaluation.R