forecast.nnetar: Forecasting using neural network models

In forecast: Forecasting Functions for Time Series and Linear Models

Forecasting using neural network models

Description

Returns forecasts and other information for univariate neural network models.

Usage

## S3 method for class 'nnetar'
forecast(
  object,
  h = ifelse(object$m > 1, 2 * object$m, 10),
  PI = FALSE,
  level = c(80, 95),
  fan = FALSE,
  xreg = NULL,
  lambda = object$lambda,
  bootstrap = FALSE,
  npaths = 1000,
  innov = NULL,
  ...
)

Arguments

object	An object of class "nnetar" resulting from a call to nnetar.
h	Number of periods for forecasting. If xreg is used, h is ignored and the number of forecast periods is set to the number of rows of xreg.
PI	If TRUE, prediction intervals are produced, otherwise only point forecasts are calculated. If PI is FALSE, then level, fan, bootstrap and npaths are all ignored.
level	Confidence level for prediction intervals.
fan	If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
xreg	Future values of external regressor variables.
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.
bootstrap	If TRUE, then prediction intervals computed using simulations with resampled residuals rather than normally distributed errors. Ignored if innov is not NULL.
npaths	Number of sample paths used in computing simulated prediction intervals.
innov	Values to use as innovations for prediction intervals. Must be a matrix with h rows and npaths columns (vectors are coerced into a matrix). If present, bootstrap is ignored.
...	Additional arguments passed to simulate.nnetar

Details

Prediction intervals are calculated through simulations and can be slow. Note that if the network is too complex and overfits the data, the residuals can be arbitrarily small; if used for prediction interval calculations, they could lead to misleadingly small values. It is possible to use out-of-sample residuals to ameliorate this, see examples.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

The generic accessor functions fitted.values and residuals extract useful features of the value returned by forecast.nnetar.

An object of class "forecast" is a list containing at least the following elements:

model	A list containing information about the fitted model
method	The name of the forecasting method as a character string
mean	Point forecasts as a time series
lower	Lower limits for prediction intervals
upper	Upper limits for prediction intervals
level	The confidence values associated with the prediction intervals
x	The original time series (either object itself or the time series used to create the model stored as object).
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

See Also

nnetar.

Examples

## Fit & forecast model
fit <- nnetar(USAccDeaths, size=2)
fcast <- forecast(fit, h=20)
plot(fcast)

## Not run: 
## Include prediction intervals in forecast
fcast2 <- forecast(fit, h=20, PI=TRUE, npaths=100)
plot(fcast2)

## Set up out-of-sample innovations using cross-validation
fit_cv <- CVar(USAccDeaths,  size=2)
res_sd <- sd(fit_cv$residuals, na.rm=TRUE)
myinnovs <- rnorm(20*100, mean=0, sd=res_sd)
## Forecast using new innovations
fcast3 <- forecast(fit, h=20, PI=TRUE, npaths=100, innov=myinnovs)
plot(fcast3)

## End(Not run)

forecast documentation built on June 22, 2024, 9:20 a.m.