def holtt(i, fc_periods, df):
df = df
hell = np.asarray(df.iloc[0:, 0])
train = df.iloc[394:, 0]
modelo = SimpleExpSmoothing(hell).fit(smoothing_level=.2, optimized=False)
resultado = modelo.fittedvalues
df['pronostico'] = resultado
nombre = list(df.columns.values.tolist())
fit1 = Holt(hell).fit(smoothing_level=0.35,
smoothing_slope=0.1,
optimized=True)
print(fit1.summary())
fcast1 = fit1.forecast(fc_periods)
return (fcast1)
'r2': r2
},
ignore_index=True)
end = timer()
print(f' Total time taken to complete grid search in seconds:{(end - start)}')
print(f' Below mentioned parameter gives least RMSE and r2')
df_results_moni.sort_values(by=['RMSE', 'r2']).head(1)
# In[57]:
fit1 = Holt(train, damped=False).fit(smoothing_level=0.9,
smoothing_slope=0.6,
damping_slope=0.1,
optimized=False)
Forecast_custom_pred = fit1.forecast(30)
fit1.summary()
# In[58]:
timeseries_evaluation_metrics_func(test, Forecast_custom_pred)
# In[59]:
# Let’s check whether the double exponential smoothing is able to find the best parameters
fitESAUTO = Holt(train).fit(optimized=True, use_brute=True)
fitESAUTO.summary()
# In[60]:
fitESAUTOpred = fitESAUTO.forecast(30)
timeseries_evaluation_metrics_func(test, fitESAUTOpred)
