class TimeSeriesEstimator does not fit to time series data properly with ensemble base estimators

[alphamupsiomega]

The TimeSeriesEstimator fits to any "X" time series matrix without needing to specify a "y" target. The TimeSeriesEstimator inherents the sklearn.base.BaseEstimator class, so any sklearn.linear_model model works fine without needing the "y" target. However, the TimeSeriesEstimator does not fit properly to just an X_train matrix if the model used is from the sklearn.ensemble methods.

How can ensemble methods be also used in TimeSeriesEstimator?

model = AdaBoostRegressor()
tsr = TimeSeriesRegressor(sklearn.base.clone(model), n_prev=n_prev)
tsr.fit(X_train)

gives the error:

pyTrading/TimeSeriesEstimator.pyc in fit(self, X, Y)
    107                 estimator.fit(X_data, Y_data[:, i])
    108         else:
--> 109             self.base_estimator.fit(X_data, Y_data)
    110 
    111         return self

//anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc in fit(self, X, y, sample_weight)
    953 
    954         # Fit
--> 955         return super(AdaBoostRegressor, self).fit(X, y, sample_weight)
    956 
    957     def _validate_estimator(self):

//anaconda/lib/python2.7/site-packages/sklearn/ensemble/weight_boosting.pyc in fit(self, X, y, sample_weight)
    109 
    110         X, y = check_X_y(X, y, accept_sparse=accept_sparse, dtype=dtype,
--> 111                          y_numeric=is_regressor(self))
    112 
    113         if sample_weight is None:

//anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    513                         dtype=None)
    514     else:
--> 515         y = column_or_1d(y, warn=True)
    516         _assert_all_finite(y)
    517     if y_numeric and y.dtype.kind == 'O':

//anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc in column_or_1d(y, warn)
    549         return np.ravel(y)
    550 
--> 551     raise ValueError("bad input shape {0}".format(shape))
    552 
    553 

ValueError: bad input shape (3130, 218)