gtime.model_selection.horizon_shift

gtime.model_selection.horizon_shift(time_series: pandas.core.frame.DataFrame, horizon: Union[int, List[int]] = 5) → pandas.core.frame.DataFrame

Perform a shift of the original time_series for each time step between 1 and horizon.

Parameters:

time_series : pd.DataFrame, shape (n_samples, n_features), required

The list of TimeSeriesFeature from which to compute the feature_extraction.

horizon : int, optional, default: 5

It represents how much into the future is necessary to predict. This corresponds to the number of shifts that are going to be performed on y.

Returns:

y : pd.DataFrame, shape (n_samples, horizon)

The shifted time series.

Examples

>>> import pandas as pd
>>> from gtime.model_selection import horizon_shift
>>> X = pd.DataFrame(range(0, 5), index=pd.date_range("2020-01-01", "2020-01-05"))
>>> horizon_shift(X, horizon=2)
            y_1  y_2
2020-01-01  1.0  2.0
2020-01-02  2.0  3.0
2020-01-03  3.0  4.0
2020-01-04  4.0  NaN
2020-01-05  NaN  NaN
>>> horizon_shift(X, horizon=[2])
            y_2
2020-01-01  2.0
2020-01-02  3.0
2020-01-03  4.0
2020-01-04  NaN
2020-01-05  NaN