AutoReg.
predict
(
params
,
start
=
None
,
end
=
None
,
dynamic
=
False
,
exog
=
None
,
exog_oos
=
None
)
[source]
In-sample prediction and out-of-sample forecasting.
Parameters
:
params
array_like
The fitted model parameters.
start
int
,
str
,
or
datetime
,
optional
Zero-indexed observation number at which to start forecasting,
i.e., the first forecast is start. Can also be a date string to
parse or a datetime type. Default is the the zeroth observation.
end
int
,
str
,
or
datetime
,
optional
Zero-indexed observation number at which to end forecasting, i.e.,
the last forecast is end. Can also be a date string to
parse or a datetime type. However, if the dates index does not
have a fixed frequency, end must be an integer index if you
want out-of-sample prediction. Default is the last observation in
the sample. Unlike standard python slices, end is inclusive so
that all the predictions [start, start+1, …, end-1, end] are
returned.
dynamic
{
bool
,
int
,
str
,
datetime
,
Timestamp
},
optional
Integer offset relative to
start
at which to begin dynamic
prediction. Prior to this observation, true endogenous values
will be used for prediction; starting with this observation and
continuing through the end of prediction, forecasted endogenous
values will be used instead. Datetime-like objects are not
interpreted as offsets. They are instead used to find the index
location of
dynamic
which is then used to to compute the offset.
exog
array_like
A replacement exogenous array. Must have the same shape as the
exogenous data array used when the model was created.
exog_oos
array_like
An array containing out-of-sample values of the exogenous variable.
Must has the same number of columns as the exog used when the
model was created, and at least as many rows as the number of
out-of-sample forecasts.
Returns
:
predictions
{
ndarray
,
Series
}
Array of out of in-sample predictions and / or out-of-sample
forecasts.
