Data mining in finance: From extremes to realism
نویسندگان
چکیده
This paper describes data mining in finance by discussing financial tasks and specifics of methodologies and techniques in this data mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based, and relational methodologies. 81 1 This paper is a modified version of authors’ chapter ‘Data mining for financial applications’ from the forthcoming ‘Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers,’ Kluwer Acad. Publ. (Eds. O.Maimon and L.Rokach). Data mining in finance: From extremes to realism ‘October. This is one of the peculiarly dangerous months to speculate in stocks in. The others are July, January, September, April, November, May, March, June, December, August, and February.’
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