def METHOD_NAME(past_ts_data: np.array, season_length: int) -> bool:
    """
    Test the time series for seasonal patterns by performing a 90% auto-
    correlation test.
    For details, see:
    http://www.unic.ac.cy/test/wp-content/uploads/sites/2/2018/09/M4-Competitors-Guide.pdf
    Code based on:
    https://github.com/Mcompetitions/M4-methods/blob/master/Benchmarks%20and%20Evaluation.R
    """
    critical_z_score = 1.645  # corresponds to 90% prediction interval
    if len(past_ts_data) < 3 * season_length:
        return False
    else:
        # calculate auto-correlation for lags up to season_length
        auto_correlations = sm.tsa.stattools.acf(
            past_ts_data, fft=False, nlags=season_length
        )
        auto_correlations[1:] = 2 * auto_correlations[1:] ** 2
        limit = (
            critical_z_score
            / np.sqrt(len(past_ts_data))
            * np.sqrt(np.cumsum(auto_correlations))
        )
        is_seasonal = (
            abs(auto_correlations[season_length]) > limit[season_length]
        )
    return is_seasonal