class_docstr: Mean absolute scaled error.
  
  Normalizes the absolute error for each time series by the historic seasonal error of this time series.
  
  .. math::
  
      \operatorname{MASE} = \frac{1}{N} \frac{1}{H} \sum_{i=1}^{N} \frac{1}{a_i} \sum_{t=T+1}^{T+H} |y_{i,t} - f_{i,t}|
  
  where :math:`a_i` is the historic absolute seasonal error defined as
  
  .. math::
  
      a_i = \frac{1}{T-m} \sum_{t=m+1}^T |y_{i,t} - y_{i,t-m}|
  
  and :math:`m` is the seasonal period of the time series (``eval_metric_seasonal_period``).
  
  Properties:
  
  - scaled metric (normalizes the error for each time series by the scale of that time series)
  - undefined for constant time series
  - not sensitive to outliers
  - prefers models that accurately estimate the median
  
  References
  ----------
  - `Wikipedia <https://en.wikipedia.org/wiki/Mean_absolute_scaled_error>`_
  - `Forecasting: Principles and Practice <https://otexts.com/fpp3/accuracy.html#scaled-errors>`_
code: def clear_past_metrics(self) -> None:
      self._past_abs_seasonal_error = None
  
imports: import logging
  from typing import Optional
  import numpy
  import pandas
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.dataset.ts_dataframe import ITEMID
  from .abstract import TimeSeriesScorer
  from .utils import _in_sample_abs_seasonal_error, _in_sample_squared_seasonal_error
label_desc: Use image node_id 3 for calling the MASE obj's underlying member method code with example usage: obj.clear_past_metrics() without return types