forecast.mlp: Forecast using MLP neural network.

In trnnick/nnfor: Time Series Forecasting with Neural Networks

Forecast using MLP neural network.

Description

Create forecasts using MLP neural networks.

Usage

## S3 method for class 'mlp'
forecast(object, h = NULL, y = NULL, xreg = NULL, ...)

Arguments

object	MLP network object, produced using mlp.
h	Forecast horizon. If NULL then h is set to match frequency of time series.
y	Optionally forecast using different data than what the network was trained on. Expected to create havoc and do really bad things!
xreg	Exogenous regressors. Each column is a different regressor and the sample size must be at least as long as the target in-sample set plus the forecast horizon, but can be longer. Set it to NULL if no xreg inputs are used.
...	Unused argument.

Value

An object of classes "forecast.net" and "forecast". The function plot produces a plot of the forecasts. An object of class "forecast.net" is a list containing the following elements:

• method - The name of the forecasting method as a character string

• mean - Point forecasts as a time series

• all.mean - An array h x reps of all ensemble members forecasts, where reps are the number of ensemble members.

• x - The original time series used to create the network.

• fitted - Fitted values.

• residuals - Residuals from the fitted network.

Author(s)

Nikolaos Kourentzes, [e-mail]

See Also

mlp, mlp.thief, elm.

Examples

## Not run: 
 fit <- mlp(AirPassengers)
 plot(fit)
 frc <- forecast(fit,h=36)
 plot(frc)

## End(Not run)

trnnick/nnfor documentation built on Nov. 12, 2023, 9:45 p.m.