def holt_winters_ci():
N, t, m = 100, 80, 4
realisations = pd.Series(list(sample_seasonal_random_walk(N,m)), range(N))
mod = ExponentialSmoothing(realisations[:t+1], trend=True, seasonal=m, initialization_method='estimated').fit(disp=False)
print(mod.summary())
forecasts = mod.get_forecast(N-(t+1))
forecasts_ci = forecasts.conf_int(alpha=0.05)
plot_ci(realisations, pd.Series(np.nan, range(t+1)).append(forecasts.predicted_mean), forecasts_ci)
py.show()
def holtwinter_model(target_variable, n_test, account_id, plot, save):
from statsmodels.tsa.api import ExponentialSmoothing
from sklearn.metrics import mean_squared_error
from math import sqrt
import numpy as np
from save_forecast import save_forecast
#Split target variable into training/test set
train, test = train_test_split(target_variable, n_test)
y_hat_avg = test.copy()
#Fit the model
fit1 = ExponentialSmoothing(
np.asarray(train[train.columns[0]]),
seasonal_periods=7,
trend='add',
seasonal='add',
).fit()
#Create forecast and save to dataframe
y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))
#Calculate RMS
rms = sqrt(mean_squared_error(test[test.columns[0]],
y_hat_avg.Holt_Winter))
#Plot results
if plot is True:
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 8))
#plt.plot(train[train.columns[0]], label='dod_model.Train')
plt.plot(test[test.columns[0]], label='Test')
plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')
plt.legend(loc='best')
plt.show()
#save forecast
if save is True:
save_forecast(y_hat_avg["Holt_Winter"], target_variable.columns[0],
"holtwinter", account_id)
return {"rms": rms, "summary": fit1.summary()}
seasonal = 'add',
trend = 'add').fit(smoothing_level=0.01,
smoothing_slope=0.1,
smoothing_seasonal=0.3)
Forecast = TES.forecast(12).rename('Forecast')
Actual_Forecast_Df = pd.concat([Fullraw3, Forecast], axis =1)
sns.lineplot(data = Actual_Forecast_Df)
Validation_Df =Actual_Forecast_Df[-12:].copy()
np.mean(abs(Validation_Df['Open'] -Validation_Df['Forecast'])/Validation_Df['Open'])*100 #MAPE################
np.sqrt(np.mean((Validation_Df['Open']- Validation_Df['Forecast'])**2))
TES2 = ExponentialSmoothing(Train,
seasonal_periods = 12,
seasonal = 'add',
trend = 'add').fit()
TES2.summary()
Forecast_2 = TES2.forecast(12).rename('Forecast_2')
Actual_Forecast_Df = pd.concat([Fullraw3, Forecast_2], axis =1)
sns.lineplot(data = Actual_Forecast_Df)
Validation_Df =Actual_Forecast_Df[-12:].copy()
np.mean(abs(Validation_Df['Sales'] -Validation_Df['Forecast_2'])/Validation_Df['Open'])*100 #MAPE################
np.sqrt(np.mean((Validation_Df['Open']- Validation_Df['Forecast_2'])**2))
# Récupérer Estimateurs
hw_aic = round(hw.aic, 2)
hw_bic = round(hw.bic, 2)
dfhw = hw.params_formatted
float(dfhw[dfhw.name == 'alpha']['param'].values)
hw_α = float(dfhw[dfhw.name == 'alpha']['param'].values)
hw_β = float(dfhw[dfhw.name == 'beta']['param'].values)
hw_γ = float(dfhw[dfhw.name == 'gamma']['param'].values)
print("Paramètres Holt-Winters : α = ", hw_α, " | β = ",hw_β, " | γ = ",hw_γ)
# In[25]:
hw.summary().tables[1]
# #### Test des Résidus "holt-winters" prédits avec Shapiro
# In[26]:
from scipy.stats import shapiro
print("TEST Shapiro P-value = ", shapiro(hw.resid)[1])
# Ici, l'hypothèse de normalité n'est pas remise en cause (p-value = 0.46 > 0.05).
# In[27]:
