title: Fast Grid Search for Holt-Winters alpha/beta/gamma parameters
body: <p>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:</p>
  
  <p>sample file:</p>
  
  <p><a href="https://ufile.io/e3zqs" rel="nofollow noreferrer">https://ufile.io/e3zqs</a></p>
  
  <pre><code>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)
  </code></pre>