def test_forecast_errors(data):
res = ThetaModel(data, period=12).fit()
with pytest.raises(ValueError, match="steps must be a positive integer"):
res.forecast(-1)
with pytest.raises(ValueError, match="theta must be a float"):
res.forecast(7, theta=0.99)
with pytest.raises(ValueError, match="steps must be a positive integer"):
res.forecast_components(0)
def test_alt_index(indexed_data):
idx = indexed_data.index
date_like = not hasattr(idx, "freq") or getattr(idx, "freq", None) is None
period = 12 if date_like else None
res = ThetaModel(indexed_data, period=period).fit()
if hasattr(idx, "freq") and idx.freq is None:
with pytest.warns(UserWarning):
res.forecast_components(37)
with pytest.warns(UserWarning):
res.forecast(23)
else:
res.forecast_components(37)
res.forecast(23)
def test_smoke(data, period, use_mle, deseasonalize, use_test, diff, model):
if period is None and isinstance(data, np.ndarray):
return
res = ThetaModel(
data,
period=period,
deseasonalize=deseasonalize,
use_test=use_test,
difference=diff,
method=model,
).fit(use_mle=use_mle)
assert "b0" in str(res.summary())
res.forecast(36)
res.forecast_components(47)
assert res.model.use_test is (use_test and res.model.deseasonalize)
assert res.model.difference is diff
def test_forecast_seasonal_alignment(data, period):
res = ThetaModel(
data,
period=period,
deseasonalize=True,
use_test=False,
difference=False,
).fit(use_mle=False)
seasonal = res._seasonal
comp = res.forecast_components(32)
index = np.arange(data.shape[0], data.shape[0] + comp.shape[0])
expected = seasonal[index % period]
np.testing.assert_allclose(comp.seasonal, expected)
res = tm.fit(use_mle=True)
print(res.summary())
# The forecast only depends on the forecast trend component,
# $$
# \hat{b}_0
# \left[h - 1 + \frac{1}{\hat{\alpha}}
# - \frac{(1-\hat{\alpha})^T}{\hat{\alpha}} \right],
# $$
#
# the forecast from the SES (which does not change with the horizon), and
# the seasonal. These three components are available using the
# `forecast_components`. This allows forecasts to be constructed using
# multiple choices of $\theta$ using the weight expression above.
res.forecast_components(12)
# ## Personal Consumption Expenditure
#
# We next look at personal consumption expenditure. This series has a
# clear seasonal component and a drift.
reader = pdr.fred.FredReader(["NA000349Q"],
start="1980-01-01",
end="2020-04-01")
pce = reader.read()
pce.columns = ["PCE"]
pce.index.freq = "QS-OCT"
_ = pce.plot()
# Since this series is always positive, we model the $\ln$.