Experimental function to forecast univariate time series with a user-defined model
modelAR(
y,
p,
P = 1,
FUN,
predict.FUN,
xreg = NULL,
lambda = NULL,
model = NULL,
subset = NULL,
scale.inputs = FALSE,
x = y,
...
)A numeric vector or time series of class ts.
Embedding dimension for non-seasonal time series. Number of non-seasonal lags used as inputs. For non-seasonal time series, the default is the optimal number of lags (according to the AIC) for a linear AR(p) model. For seasonal time series, the same method is used but applied to seasonally adjusted data (from an stl decomposition).
Number of seasonal lags used as inputs.
Function used for model fitting. Must accept argument x
and y for the predictors and response, respectively (formula
object not currently supported).
Prediction function used to apply FUN to new data.
Must accept an object of class FUN as its first argument, and a
data frame or matrix of new data for its second argument. Additionally,
it should return fitted values when new data is omitted.
Optionally, a vector or matrix of external regressors, which
must have the same number of rows as y. Must be numeric.
Box-Cox transformation parameter. If lambda="auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.
Output from a previous call to nnetar. If model is
passed, this same model is fitted to y without re-estimating any
parameters.
Optional vector specifying a subset of observations to be used
in the fit. Can be an integer index vector or a logical vector the same
length as y. All observations are used by default.
If TRUE, inputs are scaled by subtracting the column
means and dividing by their respective standard deviations. If lambda
is not NULL, scaling is applied after Box-Cox transformation.
Deprecated. Included for backwards compatibility.
Other arguments passed to FUN for
modelAR.
Returns an object of class "modelAR".
The function summary is used to obtain and print a summary of the
results.
The generic accessor functions fitted.values and residuals
extract useful features of the value returned by nnetar.
A list containing information about the fitted model
The name of the forecasting method as a character string
The original time series.
The external regressors used in fitting (if given).
Residuals from the fitted model. That is x minus fitted values.
Fitted values (one-step forecasts)
Other arguments
This is an experimental function and only recommended for advanced users.
The selected model is fitted with lagged values of y as
inputs. The inputs are for
lags 1 to p, and lags m to mP where
m=frequency(y). If xreg is provided, its columns are also
used as inputs. If there are missing values in y or
xreg, the corresponding rows (and any others which depend on them as
lags) are omitted from the fit. The model is trained for one-step
forecasting. Multi-step forecasts are computed recursively.