R: Extreme Learning Machine Forecasting

fcastelm

R Documentation

Extreme Learning Machine Forecasting

Description

The fcastelm function computes the volatility forecasting performance of Extreme Learning Machine (ELM) model with root mean square error (RMSE), mean absolute error (MAE), MAPE etc.

Usage

fcastelm(data, stepahead=6, nlags=5, freq = frequency(data),
hn=10, est=c("lm"), rep=20, combt=c("mean"))

Arguments

`data`	Univariate time series data.
`stepahead`	The forecast horizon.
`nlags`	Lags of the data to use as inputs.
`freq`	Frequency of the time series.
`hn`	Number of hidden nodes.
`est`	Estimation type for output layer weights. Can be "lasso" (lasso with CV), "ridge" (ridge regression with CV), "step" (stepwise regression with AIC) or "lm" (linear regression). Default: est=c("lm").
`rep`	Number of networks to train, the result is the ensemble forecast.
`combt`	Combination operator for forecasts when rep > 1. Can be "median", "mode" (based on KDE estimation) and "mean". Default: combt=c("mean")

Details

It helps to find the most appropriate Extreme Learning Machine model for the time series volatility forecasting.

Value

$forecast_elm: Forecasted value of Extreme Learning Machine.

$accuracy_elm: Performance matrices of ELM model

References

Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1008.

Huang, G.B., Zhu Q.Y., and Siew, C.K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489-501.

Examples

library(MSGARCHelm)
data(ReturnSeries_data)
fcastelm(ReturnSeries_data)