Measures performance at each time step ahead.
This allows to measure the performance of a model at each time step along the horizon. A copy of the provided regression metric is made for each time step. At each time step ahead, the metric is thus evaluated on each prediction for said time step, and not for the time steps before or after that.
A regression metric.
This is used internally by the
>>> from river import datasets >>> from river import metrics >>> from river import time_series >>> metric = time_series.evaluate( ... dataset=datasets.AirlinePassengers(), ... model=time_series.HoltWinters(alpha=0.1), ... metric=metrics.MAE(), ... horizon=4 ... ) >>> metric +1 MAE: 40.931286 +2 MAE: 42.667998 +3 MAE: 44.158092 +4 MAE: 43.849617
Return the current performance along the horizon.
list[float]: The current performance.
Update the metric at each step along the horizon.
- y_true ('list[Number]')
- y_pred ('list[Number]')