Abstract
We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.
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