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 train_model_autoarima(y, x, output: bool = True) -> AutoARIMA:
if output:
logger.info("Training AutoARIMA model...")
timer = Timer()
model = AutoARIMA(suppress_warnings=True, error_action='ignore')
y = pd.Series(data=np.delete(y, 0))
x = pd.DataFrame(data=x[:-1])
model.fit(y, x)
if output:
model.summary()
logger.info(f'Done in {timer}')
return model
def test_auto_arima():
"""Test bug in 805.
https://github.com/alan-turing-institute/sktime/issues/805#issuecomment-891848228.
"""
time_index = pd.date_range("January 1, 2021", periods=8, freq="1D")
X = pd.DataFrame(
np.random.randint(0, 4, 24).reshape(8, 3),
columns=["First", "Second", "Third"],
index=time_index,
)
y = pd.Series([1, 3, 2, 4, 5, 2, 3, 1], index=time_index)
fh_ = ForecastingHorizon(X.index[5:], is_relative=False)
a_clf = AutoARIMA(start_p=2, start_q=2, max_p=5, max_q=5)
clf = a_clf.fit(X=X[:5], y=y[:5])
y_pred_sk = clf.predict(fh=fh_, X=X[5:])
pd.testing.assert_index_equal(
y_pred_sk.index, pd.date_range("January 6, 2021", periods=3,
freq="1D"))
time_index = pd.date_range("January 1, 2021", periods=8, freq="2D")
X = pd.DataFrame(
np.random.randint(0, 4, 24).reshape(8, 3),
columns=["First", "Second", "Third"],
index=time_index,
)
y = pd.Series([1, 3, 2, 4, 5, 2, 3, 1], index=time_index)
fh = ForecastingHorizon(X.index[5:], is_relative=False)
a_clf = AutoARIMA(start_p=2, start_q=2, max_p=5, max_q=5)
clf = a_clf.fit(X=X[:5], y=y[:5])
y_pred_sk = clf.predict(fh=fh, X=X[5:])
pd.testing.assert_index_equal(
y_pred_sk.index, pd.date_range("January 11, 2021",
periods=3,
freq="2D"))
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), '%')