Evaluation of a Stock Trading Rule Generation Method based on Temporal Pattern Extraction

Abstract

In this paper, we present an evaluation of a temporal rule generation method for trading dataset from the Japanese stock market. Temporal data mining is one of key issues to get useful knowledge from databases. To get more valuable rules for users from a temporal data mining process, we have developed a rule generation method which consists of temporal pattern extraction methods and rule induction algorithms. Using this method, we have done a case study to evaluate temporal rules from a Japanese stock market database for trading. Based on the result, we discuss about a way to utilize our rule generation method more effectively.