MSTL Crossvalidation (refit=False) issue

[GGA-PERSO]

What happened + What you expected to happen

I raise following exception with cross validation and MSTL Model (MSTL(season_length = [12],alias='MSTL'))

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\statsforecast\models.py:5273, in MSTL.forward(self, y, h, X, X_future, level, fitted)
   5269         res = self.trend_forecaster._add_conformal_intervals(
   5270             fcst=res, y=x_sa, X=X, level=level
   5271         )
   5272 # reseasonalize results
-> 5273 seas_h = _predict_mstl_seas(model_, h=h, season_length=self.season_length)
   5274 seas_insample = model_.filter(regex="seasonal*").sum(axis=1).values
   5275 res = {
   5276     key: val + (seas_insample if "fitted" in key else seas_h)
   5277     for key, val in res.items()
   5278 }

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\statsforecast\models.py:4999, in _predict_mstl_seas(mstl_ob, h, season_length)
   4998 def _predict_mstl_seas(mstl_ob, h, season_length):
-> 4999     seascomp = _predict_mstl_components(mstl_ob, h, season_length)
   5000     return seascomp.sum(axis=1)

File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\statsforecast\models.py:4992, in _predict_mstl_components(mstl_ob, h, season_length)
   4990     mp = seasonal_periods[i]
   4991     colname = seasoncolumns[i]
-> 4992     seascomp[:, i] = np.tile(
   4993         mstl_ob[colname].values[-mp:], trunc(1 + (h - 1) / mp)
   4994     )[:h]
   4995 return seascomp

ValueError: could not broadcast input array from shape (10,) into shape (12,)

TIME SERIE LENGTH = 25Months
TEST SIZE CV = window_size_for_cv + step_size_for_cv * (nb_windows_for_cv - 1) = 14

What let me think that there is an issue is if you reduce the number of month of data to 15 then it works. If it doesn't work for 25Months I don't understand why it could work with less data with the same CV configuration

Versions / Dependencies

StatsForecast 2.0.0

Reproducible example

  
import pandas as pd
from statsforecast import StatsForecast
from statsforecast.models import (Naive,MSTL) 
from utilsforecast.data import generate_series

freq = 'MS'
season_length = 12
min_length = 25

df = generate_series(n_series=1, freq=freq, min_length=min_length, max_length=min_length)

sffcst = StatsForecast(models = [MSTL(season_length = [season_length])], freq = freq, n_jobs=-1,fallback_model=Naive(),verbose=True)
sf_crossvalidation_df=sffcst.cross_validation(df = df, h=12, step_size = 1, n_windows = 3, refit=False).reset_index(drop=True)
sf_crossvalidation_df

Issue Severity

Medium: It is a significant difficulty but I can work around it.

[GGA-PERSO]

probably linked to the refit=False but i don't understand