class_docstr: This is a class for detecting one-dimensional outliers using z-score (based on the normal distribution).
  See https://en.wikipedia.org/wiki/Standard_score as a reference.
  
  This is a child of `~greykite.common.features.outlier.ZScoreOutlierDetector`,
  which already implements a few processing steps which are useful across
  various outlier detection methods:
  
      - `remove_na`: removing NAs
      - `trim`: trimming the data. This is useful for example when using z-score
      - `diff_from_baseline`: fitting a simple baseline and differencing the input from baseline.
  
  For this method:
  
  - `DetectionResult.scores` are defined as:
      The difference of the value with trimmed mean divided by the trimmed standard deviation
  - `DetectionResult.is_outlier` are defined as: scores which are off by more than `z_score_cutoff`.
  
  Parameters
  ----------
  z_score_cutoff : `float`, default `Z_SCORE_CUTOFF`
      The normal distribution cut-off used to decide outliers.
  
  Attribute
  ---------
  fitted_param: `dict` or None, default None
      This is updated after `self.fit` is run on data.
      This dictionary stores:
  
          - "trimmed_mean": `float`
              Trimmed mean of the input fit data (after trimming).
          - "trimmed_sd": `float`
              Trimmed standard deviation of the input fit data (after trimming).
  
  
  Other Parameters and Attributes:
      See `~greykite.common.features.outlier.BaseOutlierDetector` docstring.
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docstr: See class attributes for details on parameters / attributes.
docstr_tok: ["See","class","attributes","for","details","on","parameters","\/","attributes","."]
code: def __init__(
      self,
      z_score_cutoff=Z_SCORE_CUTOFF,
      trim_percent=TRIM_PERCENT,
      diff_method=MOVING_MEDIAN,
  ):
      """See class attributes for details on parameters / attributes."""
      self.z_score_cutoff = z_score_cutoff
  
      super().__init__(
          trim_percent=trim_percent, diff_method=diff_method
      )
  
imports: import warnings
  from abc import abstractmethod
  from dataclasses import dataclass
  from dataclasses import field
  from typing import Optional
  import numpy
  import pandas
label_desc: Use image node_id 1 to create a new ZScoreOutlierDetector object from inherited base classes: BaseOutlierDetector with example: obj = ZScoreOutlierDetector(z_score_cutoff, trim_percent, diff_method)