def test_lagged_prediction_data_stochastic_series(self):
        """
        Tests that `create_lagged_prediction_data` is correctly vectorised
        over the sample axes of the input `TimeSeries`.
        """
        # Define two simple tabularization problems:
        target_1 = past_1 = future_1 = linear_timeseries(start=0, end=5)
        target_2 = past_2 = future_2 = 2 * target_1
        target = target_1.concatenate(target_2, axis=2)
        past = past_1.concatenate(past_2, axis=2)
        future = future_1.concatenate(future_2, axis=2)
        lags = lags_past = lags_future = [-1]
        # Expected solution:
        expected_X = np.concatenate(3 * [target.all_values(copy=False)], axis=1)
        # Need to account for fact that we can create feature for last time in series:
        expected_times = target_1.append_values([0]).time_index[1:]
        X, times = create_lagged_prediction_data(
            target_series=target,
            past_covariates=past,
            future_covariates=future,
            lags=lags,
            lags_past_covariates=lags_past,
            lags_future_covariates=lags_future,
            uses_static_covariates=False,
        )
        assert np.allclose(X, expected_X)
        assert times[0].equals(expected_times)

        Tests that `create_lagged_prediction_data` is correctly vectorised
        over the sample axes of the input `TimeSeries`.
        
darts/tests/utils/tabularization/test_create_lagged_prediction_data.py