gluonts.model.rotbaum package

class gluonts.model.rotbaum.TreeEstimator(**kwargs)[source]

Bases: gluonts.model.rotbaum._estimator.ThirdPartyEstimator

freq = None
lead_time = None
prediction_length = None
class gluonts.model.rotbaum.TreePredictor(freq: str, prediction_length: int, n_ignore_last: int = 0, lead_time: int = 0, max_n_datapts: int = 1000000, min_bin_size: int = 100, context_length: Optional[int] = None, use_feat_static_real: bool = False, use_feat_dynamic_real: bool = False, use_feat_dynamic_cat: bool = False, cardinality: Union[List[int], gluonts.model.rotbaum._preprocess.CardinalityLabel] = 'auto', one_hot_encode: bool = False, model_params: Optional[dict] = None, max_workers: Optional[int] = None, method: str = 'QRX', quantiles=None, model=None, seed=None)[source]

Bases: gluonts.model.predictor.RepresentablePredictor

A predictor that uses a QRX model for each of the steps in the forecast horizon. (In other words, there’s a total of prediction_length many models being trained. In particular, this predictor does not learn a multivariate distribution.) The list of these models is saved under self.model_list.

predict(dataset: gluonts.dataset.common.Dataset, num_samples: Optional[int] = None) → Iterator[gluonts.model.forecast.Forecast][source]

Returns a dictionary taking each quantile to a list of floats, which are the predictions for that quantile as you run over (time_steps, time_series) lexicographically. So: first it would give the quantile prediction for the first time step for all time series, then the second time step for all time series ˜˜ , and so forth.

train(training_data, train_QRX_only_using_timestep: int = -1)[source]