class_docstr: Class for estimating the delta for MSES.
class_docstr_tok: ["Class","for","estimating","the","delta","for","MSES","."]
docstr: Uses recent forecast errors to improve the delta estimator. This is done by updating
  the recency_weight that is used by delta stats at particular scales.
  
  :param scale_losses: A dictionary mapping a scale to a list of forecasting errors
      that associated with that scale.
code: def process_losses(
      self,
      scale_losses: Dict[int, List[float]],
      tune_recency_weights: bool = False,
  ):
      """
      Uses recent forecast errors to improve the delta estimator. This is done by updating
      the recency_weight that is used by delta stats at particular scales.
  
      :param scale_losses: A dictionary mapping a scale to a list of forecasting errors
          that associated with that scale.
      """
      for scale, losses in scale_losses.items():
          stat = self.stats.get(scale)
          stat.update_loss(losses)
          if tune_recency_weights:
              stat.tune(losses, self.eta)
  
imports: import logging
  from math import floor
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from merlion.utils.time_series import TimeSeries, UnivariateTimeSeries, assert_equal_timedeltas
  from merlion.utils.istat import ExponentialMovingAverage, RecencyWeightedVariance
  from merlion.utils.resample import to_pd_datetime, to_timestamp
  from merlion.transform.moving_average import LagTransform
  from merlion.transform.resample import TemporalResample
  from merlion.models.forecast.base import ForecasterBase, ForecasterConfig
label_desc: Use image node_id 10 for calling the DeltaEstimator obj's underlying member method code with example usage: obj.process_losses(scale_losses, tune_recency_weights) without return types