def test_pipeline():
y = load_airline()
y_train, y_test = temporal_train_test_split(y)
forecaster = TransformedTargetForecaster([
("t1", Deseasonalizer(sp=12, model="multiplicative")),
("t2", Detrender(PolynomialTrendForecaster(degree=1))),
("forecaster", NaiveForecaster()),
])
fh = np.arange(len(y_test)) + 1
forecaster.fit(y_train, fh=fh)
actual = forecaster.predict()
def compute_expected_y_pred(y_train, fh):
# fitting
yt = y_train.copy()
t1 = Deseasonalizer(sp=12, model="multiplicative")
yt = t1.fit_transform(yt)
t2 = Detrender(PolynomialTrendForecaster(degree=1))
yt = t2.fit_transform(yt)
forecaster = NaiveForecaster()
forecaster.fit(yt, fh=fh)
# predicting
y_pred = forecaster.predict()
y_pred = t2.inverse_transform(y_pred)
y_pred = t1.inverse_transform(y_pred)
return y_pred
expected = compute_expected_y_pred(y_train, fh)
np.testing.assert_array_equal(actual, expected)
def fit(self, y, X=None, fh=None):
"""Fit to training data.
Parameters
----------
y : pd.Series
Target time series to which to fit the forecaster.
fh : int, list or np.array, optional (default=None)
The forecasters horizon with the steps ahead to to predict.
X : pd.DataFrame, optional (default=None)
Exogenous variables are ignored
Returns
-------
self : returns an instance of self.
"""
y, _ = check_y_X(y, X)
sp = check_sp(self.sp)
if sp > 1 and not self.deseasonalize:
warn("`sp` is ignored when `deseasonalise`=False")
if self.deseasonalize:
self.deseasonalizer_ = Deseasonalizer(sp=self.sp,
model="multiplicative")
y = self.deseasonalizer_.fit_transform(y)
# fit exponential smoothing forecaster
# find theta lines: Theta lines are just SES + drift
super(ThetaForecaster, self).fit(y, fh=fh)
self.initial_level_ = self._fitted_forecaster.params["smoothing_level"]
# compute trend
self.trend_ = self._compute_trend(y)
self._is_fitted = True
return self
def test_deseasonalised_values(sp):
transformer = Deseasonalizer(sp=sp)
transformer.fit(y_train)
actual = transformer.transform(y_train)
r = seasonal_decompose(y_train, period=sp)
expected = y_train - r.seasonal
np.testing.assert_array_equal(actual, expected)
def compute_expected_y_pred(y_train, fh):
# fitting
yt = y_train.copy()
t1 = Deseasonalizer(sp=12, model="multiplicative")
yt = t1.fit_transform(yt)
t2 = Detrender(PolynomialTrendForecaster(degree=1))
yt = t2.fit_transform(yt)
forecaster = NaiveForecaster()
forecaster.fit(yt, fh=fh)
# predicting
y_pred = forecaster.predict()
y_pred = t2.inverse_transform(y_pred)
y_pred = t1.inverse_transform(y_pred)
return y_pred
def test_transform_inverse_transform_equivalence(sp, model):
transformer = Deseasonalizer(sp=sp, model=model)
transformer.fit(y_train)
yit = transformer.inverse_transform(transformer.transform(y_train))
np.testing.assert_array_equal(y_train.index, yit.index)
np.testing.assert_array_almost_equal(y_train, yit)
def test_inverse_transform_time_index(sp, model):
transformer = Deseasonalizer(sp=sp, model=model)
transformer.fit(y_train)
yit = transformer.inverse_transform(y_test)
np.testing.assert_array_equal(yit.index, y_test.index)