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 _theta_forecast(self, series):
period = self._analysis['theta_period']
steps = len(series)
model = ThetaModel(
series, period=period, deseasonalize=True, use_test=False
).fit()
forecast = model.forecast(steps=steps, theta=20)
return forecast
def _theta_forecast(self, series):
period = self._analysis['theta_period']
steps = len(series)
# replace last value of series by a mean value
# to avoid some extreme cases where the foecast starts at a single
# which may happen for very noisy data
# series[0] = series[::period].mean()
# series[-1] = series[::-period].mean()
model = ThetaModel(series,
period=period,
deseasonalize=True,
use_test=False).fit()
forecast = model.forecast(steps=steps, theta=20)
return forecast
tm = ThetaModel(housing)
res = tm.fit()
print(res.summary())
# The model is first and foremost a forecasting method. Forecasts are
# produced using the `forecast` method from fitted model. Below we produce a
# hedgehog plot by forecasting 2-years ahead every 2 years.
#
# **Note**: the default $\theta$ is 2.
forecasts = {"housing": housing}
for year in range(1995, 2020, 2):
sub = housing[:str(year)]
res = ThetaModel(sub).fit()
fcast = res.forecast(24)
forecasts[str(year)] = fcast
forecasts = pd.DataFrame(forecasts)
ax = forecasts["1995":].plot(legend=False)
children = ax.get_children()
children[0].set_linewidth(4)
children[0].set_alpha(0.3)
children[0].set_color("#000000")
ax.set_title("Housing Starts")
plt.tight_layout(pad=1.0)
# We could alternatively fit the log of the data. Here it makes more
# sense to force the deseasonalizing to use the additive method, if needed.
# We also fit the model parameters using MLE. This method fits the IMA
#
# $$ X_t = X_{t-1} + \gamma\epsilon_{t-1} + \epsilon_t $$