class_docstr: "AutoKeras time series data classification class.
  
  # Arguments
      column_names: A list of strings specifying the names of the columns. The
          length of the list should be equal to the number of columns of the
          data. Defaults to None. If None, it will be obtained from the
          header of the csv file or the pandas.DataFrame.
      column_types: Dict. The keys are the column names. The values should
          either be 'numerical' or 'categorical', indicating the type of that
          column. Defaults to None. If not None, the column_names need to be
          specified.  If None, it will be inferred from the data.
      lookback: Int. The range of history steps to consider for each
          prediction. For example, if lookback=n, the data in the range of [i
          - n, i - 1] is used to predict the value of step i. If unspecified,
          it will be tuned automatically.
      predict_from: Int. The starting point of the forecast for each sample
          (in number of steps) after the last time step in the input. If N is
          the last step in the input, then the first step of the predicted
          output will be N + predict_from. Defaults to 1 (which corresponds to
          starting the forecast immediately after the last step in the input).
      predict_until: Int. The end point of the forecast for each sample (in
          number of steps) after the last time step in the input. If N is the
          last step in the input, then the last step of the predicted output
          will be N + predict_until. If unspecified, it will predict till end
          of dataset.  Defaults to None.
      loss: A Keras loss function. Defaults to use 'mean_squared_error'.
      metrics: A list of Keras metrics. Defaults to use 'mean_squared_error'.
      project_name: String. The name of the AutoModel. Defaults to
          'time_series_forecaster'.
      max_trials: Int. The maximum number of different Keras Models to try.
          The search may finish before reaching the max_trials. Defaults to
          100.
      directory: String. The path to a directory for storing the search
          outputs. Defaults to None, which would create a folder with the
          name of the AutoModel in the current directory.
      objective: String. Name of model metric to minimize
          or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
      overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
          project of the same name if one is found. Otherwise, overwrites the
          project.
      seed: Int. Random seed.
      max_model_size: Int. Maximum number of scalars in the parameters of a
          model. Models larger than this are rejected.
      **kwargs: Any arguments supported by AutoModel.
code: def __init__(
      self,
      output_dim=None,
      column_names=None,
      column_types=None,
      lookback=None,
      predict_from=1,
      predict_until=None,
      loss="mean_squared_error",
      metrics=None,
      project_name="time_series_classifier",
      max_trials=100,
      directory=None,
      objective="val_loss",
      overwrite=False,
      seed=None,
      max_model_size: Optional[int] = None,
      **kwargs,
  ):
      raise NotImplementedError
  
imports: from pathlib import Path
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Type
  from typing import Union
  import pandas
  from autokeras import blocks
  from autokeras import nodes
  from autokeras.engine import tuner
  from autokeras.tasks import structured_data
  from autokeras.tuners import greedy
  from autokeras.utils import types
label_desc: Use image node_id 1 to create a new TimeseriesClassifier object from inherited base classes: SupervisedTimeseriesDataPipeline with example: obj = TimeseriesClassifier(output_dim, column_names, column_types, lookback, predict_from, predict_until, loss, metrics, project_name, max_trials, directory, objective, overwrite, seed, max_model_size)