# modelAR

##### Time Series Forecasts with a user-defined model

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

- Keywords
- ts

##### 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`

.

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

*Documentation reproduced from package forecast, version 8.13, License: GPL-3*