fcastelm: Extreme Learning Machine Forecasting

In MSGARCHelm: Hybridization of MS-GARCH and ELM Model

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)

MSGARCHelm documentation built on Oct. 24, 2020, 9:10 a.m.