gluonts.model.naive_2 package

gluonts.model.naive_2.naive_2(past_ts_data: numpy.ndarray, prediction_length: int, freq: Optional[str] = None, season_length: Optional[int] = None) → numpy.ndarray[source]

Make seasonality adjusted time series prediction.

If specified, season_length takes precedence.

As described here: https://www.m4.unic.ac.cy/wp-content/uploads/2018/03/M4-Competitors-Guide.pdf Code based on: https://github.com/Mcompetitions/M4-methods/blob/master/Benchmarks%20and%20Evaluation.R

class gluonts.model.naive_2.Naive2Predictor(freq: str, prediction_length: int, season_length: Optional[int] = None)[source]

Bases: gluonts.model.predictor.RepresentablePredictor

Naïve 2 forecaster as described in the M4 Competition Guide: https://www.m4.unic.ac.cy/wp-content/uploads/2018/03/M4-Competitors-Guide.pdf

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

Parameters
  • freq – Frequency of the input data

  • 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]