class_docstr: Time series forest regressor.
  
  A time series forest is an ensemble of decision trees built on random intervals.
  
  Overview: For input data with n series of length m, for each tree:
  
  - sample sqrt(m) intervals,
  - find mean, std and slope for each interval, concatenate to form new data set,
  - build decision tree on new data set.
  
  Ensemble the trees with averaged probability estimates.
  
  This implementation deviates from the original in minor ways. It samples
  intervals with replacement and does not use the splitting criteria tiny
  refinement described in [1]_. This is an intentionally stripped down, non
  configurable version for use as a HIVE-COTE component.
  
  Parameters
  ----------
  n_estimators : int, default=200
      Number of estimators.
  min_interval : int, default=3
      Minimum width of an interval.
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int, default=None
  
  Attributes
  ----------
  n_classes : int
      Number of classes.
  n_intervals : int
      Number of intervals.
  classes_ : list
      List of classes for a given problem.
  
  See Also
  --------
  TimeSeriesForestClassifier
  
  References
  ----------
  .. [1] H.Deng, G.Runger, E.Tuv and M.Vladimir, "A time series forest for
     classification and feature extraction", Information Sciences, 239, 2013
  .. [2] Java implementation https://github.com/uea-machine-learning/tsml
  .. [3] Arxiv paper: https://arxiv.org/abs/1302.2277
  
  Examples
  --------
  >>> from sktime.regression.interval_based import TimeSeriesForestRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> X_test, y_test = load_unit_test(split="test")
  >>> regressor = TimeSeriesForestRegressor(n_estimators=150) # doctest: +SKIP
  >>> regressor.fit(X_train, y_train) # doctest: +SKIP
  TimeSeriesForestRegressor(n_estimators=150)
  >>> y_pred = regressor.predict(X_test) # doctest: +SKIP
docstr: Override sklearn forest predict with BaseRegressor predict.
docstr_tok: ["Override","sklearn","forest","predict","with","BaseRegressor","predict","."]
code: def predict(self, X):
      """Override sklearn forest predict with BaseRegressor predict."""
      return BaseRegressor.predict(self, X)
  
file_path: sktime/sktime/regression/interval_based/_tsf.py
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.ensemble._forest import ForestRegressor
  from sklearn.tree import DecisionTreeRegressor
  from sktime.regression.base import BaseRegressor
  from sktime.series_as_features.base.estimators.interval_based._tsf import BaseTimeSeriesForest, _transform
label_desc: Use image node_id 4 for calling the TimeSeriesForestRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: BaseRegressor