tempor.models.clairvoyance2.interface.horizon module¶

class tempor.models.clairvoyance2.interface.horizon.HorizonOpts(value)[source]¶

Bases: Enum

An enumeration.

N_STEP_AHEAD = 1¶

TIME_INDEX = 2¶

class tempor.models.clairvoyance2.interface.horizon.Horizon(horizon_type: HorizonOpts)[source]¶

Bases: ABC

horizon_type : HorizonOpts¶

class tempor.models.clairvoyance2.interface.horizon.NStepAheadHorizon(n_step: int)[source]¶

Bases: Horizon

n_step : int¶

horizon_type : HorizonOpts¶

class tempor.models.clairvoyance2.interface.horizon.TimeIndexHorizon(time_index_sequence: collections.abc.Sequence[pandas.core.indexes.range.RangeIndex | pandas.core.indexes.datetimes.DatetimeIndex | pandas.core.indexes.base.Index])[source]¶

Bases: Horizon

time_index_sequence : Sequence[RangeIndex | DatetimeIndex | Index]¶

classmethod future_horizon_from_dataset(data: Dataset, forecast_n_future_steps: int, time_delta: int | float | datetime64 = 1) → TimeIndexHorizon[source]¶

horizon_type : HorizonOpts¶

to_numpy_time_series(padding_indicator: float = -999.0, max_len: int | None = None)[source]¶

to_torch_time_series(padding_indicator: float = -999.0, max_len: int | None = None, **torch_tensor_kwargs)[source]¶