Table Of Contents
Table Of Contents

gluonts.model.deepstate.issm module

class gluonts.model.deepstate.issm.CompositeISSM(seasonal_issms: List[gluonts.model.deepstate.issm.SeasonalityISSM], add_trend: bool = True)[source]

Bases: gluonts.model.deepstate.issm.ISSM

DEFAULT_ADD_TREND = True
classmethod get_from_freq(freq: str, add_trend: bool = True)[source]
get_issm_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]
latent_dim() → int[source]
output_dim() → int[source]
classmethod seasonal_features(freq: str) → List[gluonts.time_feature._base.TimeFeature][source]
class gluonts.model.deepstate.issm.ISSM[source]

Bases: object

An abstract class for providing the basic structure of Innovation State Space Model (ISSM).

The structure of ISSM is given by

  • dimension of the latent state
  • transition and emission coefficents of the transition model
  • emission coefficient of the observation model
emission_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]
get_issm_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]][source]
innovation_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]
latent_dim() → int[source]
output_dim() → int[source]
transition_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol])[source]
class gluonts.model.deepstate.issm.LevelISSM[source]

Bases: gluonts.model.deepstate.issm.ISSM

emission_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
innovation_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
latent_dim() → int[source]
output_dim() → int[source]
transition_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
class gluonts.model.deepstate.issm.LevelTrendISSM[source]

Bases: gluonts.model.deepstate.issm.LevelISSM

latent_dim() → int[source]
output_dim() → int[source]
transition_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
class gluonts.model.deepstate.issm.SeasonalityISSM(num_seasons: int)[source]

Bases: gluonts.model.deepstate.issm.LevelISSM

Implements periodic seasonality which is entirely determined by the period num_seasons.

emission_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
innovation_coeff(seasonal_indicators: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) → Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol][source]
latent_dim() → int[source]
output_dim() → int[source]