Is there any way currently to deal with trendy only signal ?

[mamadyonline]

Not all signals have necessarily a seasonal component, some might be only trendy and in this case how do we go about setting the <seasonal_length> parameter?

For example, what are the right settings for the classical airline passengers data set? I tried 12 as the seasonal_length, running the robustSTL confounds trend and seasonality !

[LeeDoYup]

(In my opiniton)
if you have no seasonality,

1. Set season_len to enough length (about 12 if monthly)
2. Remove seasonal extraction parts (https://github.com/LeeDoYup/RobustSTL/blob/master/RobustSTL.py#L41-L58)

def seasonality_extraction(sample, season_len=10, K=2, H=5, ds1=50., ds2=1.):
    '''
    sample_len = len(sample)
    idx_list = np.arange(sample_len)

    def get_season_value(idx):
        idxs = get_season_idx(sample_len, idx, season_len, K, H)
        if idxs.size == 0:
            return sample[idx]

        weight_sample = sample[idxs]
        #t_idxs = [idx - (int((idx -j)/season_len)+1)*season_len for j in idxs]
        #weights = np.array(list(map(lambda j, t: bilateral_filter(j, t, sample[j], sample[t], ds1, ds2), idxs, t_idxs)))
        weights = np.array(list(map(lambda j: bilateral_filter(j, idx, sample[j], sample[idx], ds1, ds2), idxs)))
        season_value = np.sum(weight_sample * weights)/np.sum(weights)
        return season_value

    seasons_tilda = np.array(list(map(get_season_value, idx_list)))
    '''
    return np.zeros(np.shape(sample))