from statsforecast import StatsForecast
from statsforecast.models import MSTL, AutoARIMA, AutoCES, AutoETS, AutoTheta

season_length = 24
models = [
    AutoARIMA(season_length=season_length),
    AutoCES(season_length=season_length),
    AutoETS(season_length=season_length),
    AutoTheta(season_length=season_length),    
    MSTL(
        season_length=[24, 24 * 7], # (24) hours/day, (24 * 7) days/week   
        trend_forecaster=AutoARIMA(), # model used to forecast trend 
        alias='MSTL_AutoARIMA'
    ),
    MSTL(
        season_length=[24, 24 * 7], # (24) hours/day, (24 * 7) days/week   
        trend_forecaster=AutoCES(), # model used to forecast trend
        alias='MSTL_AutoCES'
    ),
    MSTL(
        season_length=[24, 24 * 7], # (24) hours/day, (24 * 7) days/week   
        trend_forecaster=AutoTheta(), # model used to forecast trend
        alias='MSTL_AutoTheta'
    )
]

sf = StatsForecast(
    models=models, # model used to fit each time series 
    freq='H', # frequency of the data
    n_jobs=-1
)

import warnings

n_windows = 26
crossvalidation_df = sf.cross_validation(
    df=features_melted_df, 
    h=24*7*4, 
    step_size=24*7, 
    n_windows=n_windows, 
)