def METHOD_NAME(data: Dict[str, pd.DataFrame], dtype_dict: Dict[str, str],
                        timeseries_settings: TimeseriesSettings, target: str) -> Dict:
    """
    This module analyzes (pre-processed) time series data and stores a few useful insights used in the rest of Lightwood's pipeline.
    :param data: dictionary with the dataset split into train, val, test subsets. 
    :param dtype_dict: dictionary with inferred types for every column.
    :param timeseries_settings: A `TimeseriesSettings` object. For more details, check `lightwood.types.TimeseriesSettings`.
    :param target: name of the target column.
    The following things are extracted from each time series inside the dataset:
      - group_combinations: all observed combinations of values for the set of `group_by` columns. The length of this list determines how many time series are in the data.
      - deltas: inferred sampling interval 
      - ts_naive_residuals: Residuals obtained from the data by a naive forecaster that repeats the last-seen value. 
      - ts_naive_mae: Mean residual value obtained from the data by a naive forecaster that repeats the last-seen value.
      - target_normalizers: objects that may normalize the data within any given time series for effective learning. See `lightwood.encoder.time_series.helpers.common` for available choices.
    :return: Dictionary with the aforementioned insights and the `TimeseriesSettings` object for future references.
    """  # noqa
    tss = timeseries_settings
    groups = get_ts_groups(data['train'], tss)
    deltas, periods, freqs = get_delta(data['train'], tss)
    normalizers = generate_target_group_normalizers(data['train'], target, dtype_dict, tss)
    if dtype_dict[target] in (dtype.integer, dtype.float, dtype.num_tsarray):
        naive_forecast_residuals, scale_factor = get_grouped_naive_residuals(data['dev'], target, tss)
        differencers = get_differencers(data['train'], target, tss.group_by)
    else:
        naive_forecast_residuals, scale_factor = {}, {}
        differencers = {}
    return {'target_normalizers': normalizers,
            'deltas': deltas,
            'tss': tss,
            'group_combinations': groups,
            'ts_naive_residuals': naive_forecast_residuals,
            'ts_naive_mae': scale_factor,
            'periods': periods,
            'sample_freqs': freqs,
            'stl_transforms': {},  # TODO: remove, or provide from outside as user perhaps
            'differencers': differencers
            }