class_docstr: For time-series specific problems, more specific treatment of the data is necessary. The following attributes         enable time-series tasks to be carried out properly.
  
  :param is_timeseries: Whether the input data should be treated as time series; if true, this flag is checked in         subsequent internal steps to ensure processing is appropriate for time-series data.
  :param order_by: Column by which the data should be ordered.
  :param group_by: Optional list of columns by which the data should be grouped. Each different combination of values         for these columns will yield a different series.
  :param window: The temporal horizon (number of rows) that a model intakes to "look back" into when making a         prediction, after the rows are ordered by the order_by column and split into groups if applicable.
  :param horizon: The number of points in the future that predictions should be made for, defaults to 1. Once         trained, the model will be able to predict up to this many points into the future.
  :param historical_columns: The temporal dynamics of these columns will be used as additional context to train the         time series predictor. Note that a non-historical column shall still be used to forecast, but without             considering their change through time.
  :param target_type: Automatically inferred dtype of the target (e.g. `dtype.integer`, `dtype.float`).
  :param use_previous_target: Use the previous values of the target column to generate predictions. Defaults to True.
  :param allow_incomplete_history: whether predictions can be made for rows with incomplete historical context (i.e. less than `window` rows have been observed for the datetime that has to be forecasted).
  :param eval_incomplete: whether to consider predictions with incomplete history or target information when evaluating mixer accuracy with the validation dataset.
  :param interval_periods: tuple of tuples with user-provided period lengths for time intervals. Default values will be added for intervals left unspecified. For interval options, check the `timeseries_analyzer.detect_period()` method documentation. e.g.: (('daily', 7),).
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docstr: Creates JSON config from TimeseriesSettings object
  :returns: The JSON config syntax containing the ``TimeSeriesSettings`` specifications.
docstr_tok: ["Creates","JSON","config","from","TimeseriesSettings","object",":","returns",":","The","JSON","config","syntax","containing","the","``","TimeSeriesSettings","``","specifications","."]
code: def to_json(self) -> Dict[str, Json]:
      """
      Creates JSON config from TimeseriesSettings object
      :returns: The JSON config syntax containing the ``TimeSeriesSettings`` specifications.
      """
      return json.dumps(self.to_dict())
  
imports: from typing import Dict, List, Optional, Union
  import sys
  from type_infer.dtype import dtype
  from dataclasses import dataclass
  from lightwood.helpers.log import log
  from dataclasses_json import dataclass_json
  from dataclasses_json.core import _asdict, Json
  import json
label_desc: Use image node_id 4 for calling the TimeseriesSettings obj's underlying member method code with example usage: obj.to_json() and returns: json