gluonts.torch.model.forecast module

class gluonts.torch.model.forecast.DistributionForecast(distribution: Distribution, start_date: Period, item_id: Optional[str] = None, info: Optional[Dict] = None)

Bases: Forecast

A Forecast object that uses a distribution directly.

This can for instance be used to represent marginal probability distributions for each time point – although joint distributions are also possible, e.g. when using MultiVariateGaussian).

Parameters:

• distribution –

  Distribution object. This should represent the entire prediction length, i.e., if we draw num_samples samples from the distribution, the sample shape should be

  samples = trans_dist.sample(num_samples) samples.shape -> (num_samples, prediction_length)

• start_date (pandas._libs.tslibs.period.Period) – start of the forecast

• info (Optional[Dict]) – additional information that the forecaster may provide e.g. estimated parameters, number of iterations ran etc.

property mean: ndarray

Forecast mean.

property mean_ts: Series

Forecast mean, as a pandas.Series object.

quantile(level: Union[float, str]) → ndarray

Compute a quantile from the predicted distribution.

Parameters:

q – Quantile to compute.

Returns:

Value of the quantile across the prediction range.

Return type:

numpy.ndarray

to_sample_forecast(num_samples: int = 200) → SampleForecast