Publication Type:

Journal Article


Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer-Verlag Berlin Heidelberg, Volume 6466 LNCS, p.567-574 (2010)


3642175627; 9783642175626



Artificial intelligence, Backward elimination, Brute force, Computational intelligence, Cross over, Data mining, Data reduction, Evolutionary algorithms, Execution time, Experimental studies, Financial data processing, Financial time series, Forecasting, Forward selection, Open sources, Optimization, Optimized selection, Performance comparison, Real time, Reduction algorithms, Research problems, Root mean squared errors, Selection algorithm, Selection scheme, Short term, Squared errors, Stock data, Stock price prediction, Stock trend prediction, Time series, Time series analysis


Stock price prediction and stock trend prediction are the two major research problems of financial time series analysis. In this work, performance comparison of various attribute set reduction algorithms were made for short term stock price prediction. Forward selection, backward elimination, optimized selection, optimized selection based on brute force, weight guided and optimized selection based on the evolutionary principle and strategy was used. Different selection schemes and cross over types were explored. To supplement learning and modeling, support vector machine was also used in combination. The algorithms were applied on a real time Indian stock data namely CNX Nifty. The experimental study was conducted using the open source data mining tool Rapidminer. The performance was compared in terms of root mean squared error, squared error and execution time. The obtained results indicates the superiority of evolutionary algorithms and the optimize selection algorithm based on evolutionary principles outperforms others. © 2010 Springer-Verlag.


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Cite this Research Publication

P. Bhagavathi Sivakumar and Mohandas, V. Pb, “Performance comparison of attribute set reduction algorithms in stock price prediction - A case study on Indian stock data”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 567-574, 2010.