def forecasting_autoarima(y_train, y_test, s):
fh = np.arange(len(y_test)) + 1
forecaster = AutoARIMA(sp=s)
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
plot_ys(y_train, y_test, y_pred, labels=["y_train", "y_test", "y_pred"])
st.pyplot()
def main():
df = datasets.load_airline(
) #Univariate, monthly records from 1949 to 60 (144 records)
y_train, y_test = temporal_train_test_split(
df, test_size=36) #36 months for testing
forecaster = NaiveForecaster(
strategy='seasonal_last', sp=12
) #model strategy: last, mean, seasonal_last. sp=12months (yearly season)
forecaster.fit(y_train) #fit
fh = np.arange(1,
len(y_test) +
1) #forecast horizon: array with the same lenght of y_test
y_pred = forecaster.predict(fh) #pred
forecaster2 = AutoARIMA(sp=12, suppress_warnings=True, trace=1)
forecaster2.fit(y_train)
y_pred2 = forecaster2.predict(fh)
forecaster3 = ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)
forecaster3.fit(y_train)
y_pred3 = forecaster3.predict(fh)
forecaster4 = ThetaForecaster(sp=12)
forecaster4.fit(y_train)
y_pred4 = forecaster4.predict(fh)
forecaster5 = EnsembleForecaster([
('NaiveForecaster', NaiveForecaster(strategy='seasonal_last', sp=12)),
('AutoARIMA', AutoARIMA(sp=12, suppress_warnings=True)),
('Exp Smoothing',
ExponentialSmoothing(trend='add',
damped='True',
seasonal='multiplicative',
sp=12)), ('Theta', ThetaForecaster(sp=12))
])
forecaster5.fit(y_train)
y_pred5 = forecaster5.predict(fh)
plot_ys(y_train,
y_test,
y_pred,
y_pred2,
y_pred3,
y_pred4,
y_pred5,
labels=[
'Train', 'Test', 'Naive Forecaster', 'AutoARIMA',
'Exp Smoothing', 'Theta', 'Ensemble'
])
plt.xlabel('Months')
plt.ylabel('Number of flights')
plt.title(
'Time series of the number of international flights in function of time'
)
plt.show()
print('SMAPE Error for NaiveForecaster is:',
100 * round(smape_loss(y_test, y_pred), 3), '%')
print('SMAPE Error for AutoARIMA is:',
100 * round(smape_loss(y_test, y_pred2), 3), '%')
print('SMAPE Error for Exp Smoothing is:',
100 * round(smape_loss(y_test, y_pred3), 3), '%')
print('SMAPE Error for Theta is:',
100 * round(smape_loss(y_test, y_pred4), 3), '%')
print('SMAPE Error for Ensemble is:',
100 * round(smape_loss(y_test, y_pred5), 3), '%')