FORECASTING KUALA LUMPUR COMPOSITE INDEX: EVIDENCE OF THE ARTIFICIAL NEURAL NETWORK AND ARIMA

The aim of this paper is to use, compare, and analyze two forecasting technique: namely
Auto Regressive Integrated Moving Average(ARIMA) and Artificial Neural
Network(ANN) using Kuala Lumpur Composite Index(KLCI) in Malaysia. Artificial
Neural Network is used because of its popularity of capturing the volatility patterns in
nonlinear time series while ARIMA used since it is a standard method in the forecasting
tool. Daily data of Kuala Lumpur Composite Index from 4 January 1999 to 26 September
2005 is used. ANN training with "early stopping” technique is investigated. We found
that the deviation or error showed in the ANN technique is much less than that in
ARIMA. Hence ANN can be used as a good forecaster engine for univariate time series
model. It can predict nonlinear time series using the pattern of the past data. The
proposed technique may help government, decision makers and planners especially in
Malaysia.
Keyword : Auto Regressive Integrated Moving Average(ARIMA), artificial neural
network(ANN) and Kuala Lumpur Composite Index(KLCI)