def fit(self, series: TimeSeries):
super().fit(series)
series._assert_univariate()
series = self.training_series
if self.version == "tsb":
self.forecast_val = self.method(
series.values(copy=False),
h=1,
future_xreg=None,
alpha_d=self.alpha_d,
alpha_p=self.alpha_p,
)
elif self.version == "sba":
try:
self.forecast_val = self.method(series.values(copy=False),
h=1,
future_xreg=None)
except errors.TypingError:
raise_if(
True,
'"sba" version is not supported with this version of statsforecast.',
)
else:
self.forecast_val = self.method(series.values(copy=False),
h=1,
future_xreg=None)
return self
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