def helper_test_intersect(test_case, test_series: TimeSeries):
seriesA = TimeSeries.from_series(
pd.Series(range(2, 8), index=pd.date_range('20130102',
'20130107')))
seriesB = test_series.slice_intersect(seriesA)
test_case.assertEqual(seriesB.start_time(), pd.Timestamp('20130102'))
test_case.assertEqual(seriesB.end_time(), pd.Timestamp('20130107'))
# Outside of range
seriesD = test_series.slice_intersect(
TimeSeries.from_series(
pd.Series(range(6, 13),
index=pd.date_range('20130106', '20130112'))))
test_case.assertEqual(seriesD.start_time(), pd.Timestamp('20130106'))
test_case.assertEqual(seriesD.end_time(), pd.Timestamp('20130110'))
# Small intersect
seriesE = test_series.slice_intersect(
TimeSeries.from_series(
pd.Series(range(9, 13),
index=pd.date_range('20130109', '20130112'))))
test_case.assertEqual(len(seriesE), 2)
# No intersect
with test_case.assertRaises(ValueError):
test_series.slice_intersect(
TimeSeries(
pd.Series(range(6, 13),
index=pd.date_range('20130116', '20130122'))))
def residuals(self,
series: TimeSeries,
forecast_horizon: int = 1,
verbose: bool = False) -> TimeSeries:
""" A function for computing the residuals produced by the current model on a univariate time series.
This function computes the difference between the actual observations from `series`
and the fitted values vector `p` obtained by training the model on `series`.
For every index `i` in `series`, `p[i]` is computed by training the model on
`series[:(i - forecast_horizon)]` and forecasting `forecast_horizon` into the future.
(`p[i]` will be set to the last value of the predicted series.)
The vector of residuals will be shorter than `series` due to the minimum
training series length required by the model and the gap introduced by `forecast_horizon`.
Most commonly, unless otherwise specified, the term "residuals" implies a value for `forecast_horizon` of 1.
This method works only on univariate series and does not currently support covariates.
Parameters
----------
series
The univariate TimeSeries instance which the residuals will be computed for.
forecast_horizon
The forecasting horizon used to predict each fitted value.
verbose
Whether to print progress.
Returns
-------
TimeSeries
The vector of residuals.
"""
series._assert_univariate()
# get first index not contained in the first training set
first_index = series.time_index()[self.min_train_series_length]
# compute fitted values
p = self.historical_forecasts(series=series,
start=first_index,
forecast_horizon=forecast_horizon,
stride=1,
retrain=True,
last_points_only=True,
verbose=verbose)
# compute residuals
series_trimmed = series.slice_intersect(p)
residuals = series_trimmed - p
return residuals