class_docstr: The base class for filtering models. It defines the *minimal* behavior that all filtering models
  have to support. The filtering models are all "local" models; meaning they act on one time series alone.
docstr: Filters a given series
  
  Parameters
  ----------
  series
      The series to filter.
  
  Returns
  -------
  TimeSeries
      A time series containing the filtered values.
docstr_tok: ["Filters","a","given","series","Parameters","--","--","--","--","--","series","The","series","to","filter",".","Returns","--","--","--","-","TimeSeries","A","time","series","containing","the","filtered","values","."]
code: def filter(self, series: TimeSeries) -> TimeSeries:
      """Filters a given series
  
      Parameters
      ----------
      series
          The series to filter.
  
      Returns
      -------
      TimeSeries
          A time series containing the filtered values.
      """
      raise_if_not(
          series.is_deterministic,
          "The input series must be deterministic (observations).",
      )
  
imports: from abc import ABC, abstractmethod
  from darts.logging import get_logger, raise_if_not
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 2 for calling the FilteringModel obj's underlying member method code with example usage: obj.filter(series) without return types