modelAR: Time Series Forecasts with a user-defined model

Description

Experimental function to forecast univariate time series with a user-defined model

Usage

modelAR(
  y,
  p,
  P = 1,
  FUN,
  predict.FUN,
  xreg = NULL,
  lambda = NULL,
  model = NULL,
  subset = NULL,
  scale.inputs = FALSE,
  x = y,
  ...
)

Value

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.

model: A list containing information about the fitted model
method: The name of the forecasting method as a character string
x: The original time series.
xreg: The external regressors used in fitting (if given).
residuals: Residuals from the fitted model. That is x minus fitted values.
fitted: Fitted values (one-step forecasts)
...: Other arguments

Arguments

y: A numeric vector or time series of class ts.
p: 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).
P: Number of seasonal lags used as inputs.
FUN: Function used for model fitting. Must accept argument x and y for the predictors and response, respectively (formula object not currently supported).
predict.FUN: 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.
xreg: Optionally, a vector or matrix of external regressors, which must have the same number of rows as y. Must be numeric.
lambda: 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.
model: Output from a previous call to nnetar. If model is passed, this same model is fitted to y without re-estimating any parameters.
subset: 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.
scale.inputs: 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.
x: Deprecated. Included for backwards compatibility.
...: Other arguments passed to FUN for modelAR.

Author

Rob J Hyndman and Gabriel Caceres

Details

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.