Predictive fuzzy reasoning method for time series stock market data mining

Rashid Hafeez Khokhar; Mohd Noor Md Sap

doi:10.1117/12.603089

28 March 2005 Predictive fuzzy reasoning method for time series stock market data mining

Rashid Hafeez Khokhar, Mohd Noor Md Sap

Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005; (2005) https://doi.org/10.1117/12.603089
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Data mining is able to uncover hidden patterns and predict future trends and behaviors in financial markets. In this research we approach quantitative time series stock selection as a data mining problem. We present another modification of extraction of weighted fuzzy production rules (WFPRs) from fuzzy decision tree by using proposed similarity-based fuzzy reasoning method called predictive reasoning (PR) method. In proposed predictive reasoning method weight parameter can be assigned to each proposition in the antecedent of a fuzzy production rule (FPR) and certainty factor (CF) to each rule. Certainty factors are calculated by using some important variables like effect of other companies, effect of other local stock market, effect of overall world situation, and effect of political situation from stock market. The predictive FDT has been tested using three data sets including KLSE, NYSE and LSE. The experimental results show that WFPRs rules have high learning accuracy and also better predictive accuracy of stock market time series data.

Citation Download Citation

Rashid Hafeez Khokhar and Mohd Noor Md Sap "Predictive fuzzy reasoning method for time series stock market data mining", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.603089

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