class_docstr: 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.
  
  Parameters
  ----------
  metric
      A regression metric.
  
  Examples
  --------
  
  This is used internally by the `time_series.evaluate` function.
  
  >>> 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
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code: def __init__(self, metric: metrics.base.RegressionMetric):
      self.metric = metric
      self.metrics: list[metrics.base.RegressionMetric] = []
  
imports: from __future__ import annotations
  import abc
  import typing
  from numbers import Number
  from river import base, metrics
label_desc: Use image node_id 1 to create a new HorizonMetric object from inherited base classes: ForecastingMetric with example: obj = HorizonMetric(metric)