I was trying this code and manage to get expected resul as seen in the picture below the code

# multiplicative seasonal component
result_mul = seasonal_decompose(data['value'][-36:],   # 3 years

seasonal_index = result_mul.seasonal[-12:].to_frame()
seasonal_index['month'] = pd.to_datetime(seasonal_index.index).month

# merge with the base data
data['month'] = data.index.month
df = pd.merge(data, seasonal_index, how='left', on='month')
df.columns = ['value', 'month', 'seasonal_index']
df.index = data.index  # reassign the index.

import pmdarima as pm

sxmodel = pm.auto_arima(df[['value']], exogenous=df[['seasonal_index']],
                        start_p=1, start_q=1,
                        max_p=3, max_q=3, m=12,
                        start_P=0, seasonal=True,
                        d=None, D=1, trace=True,


# Forecast
n_periods = 24
fitted, confint = sxmodel.predict(n_periods=n_periods,
                                  exogenous=numpy.tile(seasonal_index.seasonal, 2).reshape(-1,1),

index_of_fc = pd.date_range(data.index[-1], periods = n_periods, freq='MS')

Cross Vallidation Result

But I do not understand why I has some error after I modified the code by changing some values into:

result_mul = seasonal_decompose(data['value'][-120:], model='multiplicative', extrapolate_trend='freq')

seasonal_index = result_mul.seasonal[-90:].to_frame()

n_periods = 60

I got this kind of error

ValueError: Length mismatch: Expected axis has 3033 elements, new values have 365 elements

I am not sure why, all I did was trying to use my own data while also changing the seasonality with 3 months,apply seasonality on latest 4 months and trying forecast and cros validated the last 2 months. I did not change anything else, just the numbers.

As a note, my data is a daily data for 1 year. Meaning it has 365 data, while the old data has 205 data, monthyly data for several years to be precise.

Thanks in advance for any tips.

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