# residuals.forecast

##### Residuals for various time series models

Returns time series of residuals from a fitted model.

- Keywords
- ts

##### Usage

```
# S3 method for forecast
residuals(object, type = c("innovation", "response"), ...)
```# S3 method for ar
residuals(object, type = c("innovation", "response"), ...)

# S3 method for Arima
residuals(object, type = c("innovation", "response", "regression"), h = 1, ...)

# S3 method for bats
residuals(object, type = c("innovation", "response"), h = 1, ...)

# S3 method for tbats
residuals(object, type = c("innovation", "response"), h = 1, ...)

# S3 method for ets
residuals(object, type = c("innovation", "response"), h = 1, ...)

# S3 method for ARFIMA
residuals(object, type = c("innovation", "response"), ...)

# S3 method for nnetar
residuals(object, type = c("innovation", "response"), h = 1, ...)

# S3 method for stlm
residuals(object, type = c("innovation", "response"), ...)

# S3 method for tslm
residuals(object, type = c("innovation", "response", "deviance"), ...)

##### Arguments

- object
An object containing a time series model of class

`ar`

,`Arima`

,`bats`

,`ets`

,`arfima`

,`nnetar`

or`stlm`

. If`object`

is of class`forecast`

, then the function will return`object$residuals`

if it exists, otherwise it returns the differences between the observations and their fitted values.- type
Type of residual.

- ...
Other arguments not used.

- h
If

`type='response'`

, then the fitted values are computed for`h`

-step forecasts.

##### Details

Innovation residuals correspond to the white noise process that drives the
evolution of the time series model. Response residuals are the difference
between the observations and the fitted values (equivalent to `h`

-step
forecasts). For functions with no `h`

argument, `h=1`

. For
homoscedastic models, the innovation residuals and the response residuals
for `h=1`

are identical. Regression residuals are available for
regression models with ARIMA errors, and are equal to the original data
minus the effect of the regression variables. If there are no regression
variables, the errors will be identical to the original series (possibly
adjusted to have zero mean). `arima.errors`

is a deprecated function
which is identical to `residuals.Arima(object, type="regression")`

.
For `nnetar`

objects, when `type="innovations"`

and `lambda`

is used, a
matrix of time-series consisting of the residuals from each of the fitted neural networks is returned.

##### Value

A `ts`

object.

##### See Also

##### Examples

```
# NOT RUN {
fit <- Arima(lynx,order=c(4,0,0), lambda=0.5)
plot(residuals(fit))
plot(residuals(fit, type='response'))
# }
```

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