def test_ts_with_missing_data(self):
        """
        Check that multivariate monthly to quarterly with missing data in the middle is properly transformed.
        """
        stacked_monthly_ts_missing = self.monthly_ts.stack(
            TimeSeries.from_times_and_values(
                times=self.monthly_ts.time_index,
                values=np.array([10, 11, 12, np.nan, np.nan, 15, 16, 17, 18]),
                columns=["other"],
            )
        )

        # components are interleaved
        expected_quarterly_ts = TimeSeries.from_times_and_values(
            times=self.quarterly_ts.time_index,
            values=np.array([
                [1, 10, 2, 11, 3, 12],
                [4, np.nan, 5, np.nan, 6, 15],
                [7, 16, 8, 17, 9, 18],
            ]),
            columns=[
                "values_midas_0",
                "other_midas_0",
                "values_midas_1",
                "other_midas_1",
                "values_midas_2",
                "other_midas_2",
            ],
        )

        midas_1 = MIDAS(low_freq="QS")
        multivar_quarterly_ts_midas = midas_1.fit_transform(stacked_monthly_ts_missing)
        assert multivar_quarterly_ts_midas == expected_quarterly_ts

        multivar_inversed_quarterly_ts_midas = midas_1.inverse_transform(
            multivar_quarterly_ts_midas
        )
        assert stacked_monthly_ts_missing == multivar_inversed_quarterly_ts_midas

        Check that multivariate monthly to quarterly with missing data in the middle is properly transformed.
        
darts/tests/dataprocessing/transformers/test_midas.py