Model based multi criteria decision making methods for prediction of time series data

ABSTRACT: Financial forecasting is a difficult task due to the intrinsic complexity of the financial system, in this research the estimation of the stock exchange prices is targeted using the five-year time series data of prices. The objective of this work is to use an intelligence techniques and mathematical techniques to create a model, that has the ability to predict the future price of a stock market index, then decide throughout the k-means clustering with majority voting, which one of those prediction techniques is the best. It is a multi-decision making in order to find the best predictive method. The proposed method combines multiple methods to have higher prediction accuracy and higher profit/risk ratio. The forecasting techniques, namely, Radial Basis Function (RBF) combined with Self-organizing map, Nearest Neighbour (K-Nearest Neighbour) methods, and Autoregressive Fractionally Integrated Moving Average (ARFIMA) are implemented in forecasting the future price of a stock market index based on its historical price information, and the best forecast of these three methods is decided by majority voting after k-means clustering. The experimentation was performed on data obtained from the London Stock Exchange. The data used was a series of past closing prices of the Share Index. The results showed that the proposed decision method provides better prediction than forecasts of the three techniques. Keywords: Forecasting, SOM-RBF, K-Nearest Neighbour, ARFIMA, Decision-making. …………………………………………………………………………………………………………………………………………………………………………………………………………