ForecastingHorizon#
- class ForecastingHorizon(values: Optional[Union[int, list, numpy.ndarray, pandas.core.indexes.base.Index]] = None, is_relative: Optional[bool] = None)[source]#
Forecasting horizon.
- Parameters
- valuespd.Index, pd.TimedeltaIndex, np.array, list, pd.Timedelta, or int
Values of forecasting horizon
- is_relativebool, optional (default=None)
- If True, a relative ForecastingHorizon is created:
values are relative to end of training series.
- If False, an absolute ForecastingHorizon is created:
values are absolute.
- if None, the flag is determined automatically:
relative, if values are of supported relative index type absolute, if not relative and values of supported absolute index type
- Attributes
is_relativeWhether forecasting horizon is relative to the end of the training series.
Methods
is_all_in_sample([cutoff])Whether the forecasting horizon is purely in-sample for given cutoff.
is_all_out_of_sample([cutoff])Whether the forecasting horizon is purely out-of-sample for given cutoff.
to_absolute(cutoff)Return absolute version of forecasting horizon values.
to_absolute_int(start[, cutoff])Return absolute values as zero-based integer index starting from start.
to_in_sample([cutoff])Return in-sample index values of fh.
to_indexer([cutoff, from_cutoff])Return zero-based indexer values for easy indexing into arrays.
to_numpy(**kwargs)Return forecasting horizon’s underlying values as np.array.
to_out_of_sample([cutoff])Return out-of-sample values of fh.
Return forecasting horizon’s underlying values as pd.Index.
to_relative([cutoff])Return forecasting horizon values relative to a cutoff.
- property is_relative: bool[source]#
Whether forecasting horizon is relative to the end of the training series.
- Returns
- is_relativebool
- to_pandas() pandas.core.indexes.base.Index[source]#
Return forecasting horizon’s underlying values as pd.Index.
- Returns
- fhpd.Index
pandas Index containing forecasting horizon’s underlying values.
- to_numpy(**kwargs) numpy.ndarray[source]#
Return forecasting horizon’s underlying values as np.array.
- Parameters
- **kwargsdict of kwargs
kwargs passed to to_numpy() of wrapped pandas index.
- Returns
- fhnp.ndarray
NumPy array containg forecasting horizon’s underlying values.
- to_relative(cutoff=None)[source]#
Return forecasting horizon values relative to a cutoff.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa).
- Returns
- fhForecastingHorizon
Relative representation of forecasting horizon.
- to_absolute(cutoff)[source]#
Return absolute version of forecasting horizon values.
- Parameters
- cutoffpd.Period, pd.Timestamp, int
Cutoff value is required to convert a relative forecasting horizon to an absolute one (and vice versa).
- Returns
- fhForecastingHorizon
Absolute representation of forecasting horizon.
- to_absolute_int(start, cutoff=None)[source]#
Return absolute values as zero-based integer index starting from start.
- Parameters
- startpd.Period, pd.Timestamp, int
Start value returned as zero.
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa).
- Returns
- fhForecastingHorizon
Absolute representation of forecasting horizon as zero-based integer index.
- to_in_sample(cutoff=None)[source]#
Return in-sample index values of fh.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting horizon to an absolute one (and vice versa).
- Returns
- fhForecastingHorizon
In-sample values of forecasting horizon.
- to_out_of_sample(cutoff=None)[source]#
Return out-of-sample values of fh.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value is required to convert a relative forecasting horizon to an absolute one (and vice versa).
- Returns
- fhForecastingHorizon
Out-of-sample values of forecasting horizon.
- is_all_in_sample(cutoff=None) bool[source]#
Whether the forecasting horizon is purely in-sample for given cutoff.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, default=None
Cutoff value used to check if forecasting horizon is purely in-sample.
- Returns
- retbool
True if the forecasting horizon is purely in-sample for given cutoff.
- is_all_out_of_sample(cutoff=None) bool[source]#
Whether the forecasting horizon is purely out-of-sample for given cutoff.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value used to check if forecasting horizon is purely out-of-sample.
- Returns
- retbool
True if the forecasting horizon is purely out-of-sample for given cutoff.
- to_indexer(cutoff=None, from_cutoff=True)[source]#
Return zero-based indexer values for easy indexing into arrays.
- Parameters
- cutoffpd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting horizon to an absolute one and vice versa.
- from_cutoffbool, optional (default=True)
If True, zero-based relative to cutoff.
If False, zero-based relative to first value in forecasting
horizon.
- Returns
- fhpd.Index
Indexer.