Khokhar, Rashid Hafeez and Sap, M.N Md (2005) Predictive fuzzy reasoning method for time series stock market data mining. In: Proceedings of SPIE - The International Society for Optical Engineering , 28 March 2005 , Orlando, FL, USA .
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Official URL: http://dx.doi.org/10.1117/12.603089
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Classification, data mining, decision tree, f uzzy reasoning, rules mining, time series|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System|
|Deposited By:||Maznira Sylvia Azra Mansor|
|Deposited On:||09 Jan 2009 02:50|
|Last Modified:||09 Jan 2009 02:50|
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