I have implemented the Holt-Winters model via Statsmodels in my script and I can make predictions with it but I manually set the alpha beta and gamma hyperparameters. According to you, what would be the fastest way to get the ideal values for those parameters with my dataset and how to implement it? Is there any auto optimization for the Holt-Winters just like Auto Arima? You can find my Python code below:
sample file:
from statsmodels.tsa.api import ExponentialSmoothing
import pandas as pd
import numpy as np
df = pd.read_excel("C:\\Users\\YannickLECROART\\Documents\\Python\\temprennes.xlsx", index_col=0)
df = df.fillna(0)
df.index = pd.to_datetime(df.index)
# our guessed parameters
alpha = 0.4
beta = 0.2
gamma = 0.01
# initialise model
ets_model = ExponentialSmoothing(df_data, trend='add', seasonal='add',
seasonal_periods=12)
ets_fit = ets_model.fit(smoothing_level=alpha, smoothing_slope=beta,
smoothing_seasonal=gamma)
# forecast p hours ahead
p_ahead = 12
yh = ets_fit.forecast(p_ahead)