R: Time Series Forecasts with a user-defined model

modelAR {forecast} | R Documentation

## 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, ...)

### 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`.

### 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.

### 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

### Author(s)

Rob J Hyndman and Gabriel Caceres

[Package forecast version 8.4 Index]