MSGARCHelm (version 0.1.0)

fcastelm: 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")

Value

$forecast_elm: Forecasted value of Extreme Learning Machine.

$accuracy_elm: Performance matrices of ELM model

Details

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

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

# NOT RUN {
library(MSGARCHelm)
data(ReturnSeries_data)
fcastelm(ReturnSeries_data)
# }