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A Prediction of Attractive Evaluation Objects Based On Complex Sequential Data

Authors: Shigeaki Sakurai, Makino Kyoko, Shigeru Matsumoto

Abstract:

This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The trend rules represent changes of numerical values related to evaluation objects. In the prediction phase, the method applies new text sequential data to the trend rules and evaluates which evaluation objects are attractive. This paper verifies the effect of the proposed method by using stock price sequences and news headline sequences. In these sequences, each stock brand corresponds to an evaluation object. This paper discusses validity of predicted attractive evaluation objects, the process time of each phase, and the possibility of application tasks.

Keywords: frequent pattern, Trend rule, numerical sequential data, text sequential data, evaluation object

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1092020

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References:


[1] W. Antweiler and M. Z. Frank Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards, J. of Finance, vol.59, no.3, pp.1259-1294, 2004.
[2] J. Bollen, H. Mao, and X. -J. Zeng, Twitter Mood Predicts the Stock Market, http://arxiv.org/PS cache/arxiv/pdf/1010/1010.3003v1.pdf, October, 2010.
[3] Chasen, http://chasen.naist.jp/hiki/ChaSen/, 2010 (in Japanese).
[4] M. D. Choudhury, H. Sundaram, A. John, and D. D. Seligmann, Can Blog Communication Dynamics be Correlated with Stock Market Activity?, Proc. of the 19th ACM Conf. on Hypertext and Hypermedia, 2010.
[5] G. P. C. Fung, J. X. Yu, and W. Lam, News Sensitive Stock Trend Prediction, Proc. of the 6th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pp.481-493, 2002.
[6] J. Han, J. Pei, and Y. Yin, Mining Frequent Patterns without Candidate Generation, Proc. of the 2000 ACM SIGMOD Intl. Conf. on Management of Data, pp.1-12, 2000.
[7] M. -A. Mittermayer and G. F. Knolmayer, NewsCATS - A News Categorization and Trading System, Proc. of the 6th IEEE Intl. Conf. on Data Mining, pp.1002-1007, 2006.
[8] D. Peramunetilleke and R. K.Wong, Currency Exchange Rate Forecasting from News Headlines, Proc. of the 13th Australasian Database Conf., vol.5, pp.131-139, 2002.
[9] S. Sakurai and K. Ueno, Analysis of Daily Business Reports based on Sequential Text Mining Method, Proc. of the 2004 IEEE Intl. Conf. on Systems, Man and Cybernetics, vol.4, pp.3279-3284, 2004.
[10] S. Sakurai, Y. Kitahara, and R. Orihara, Sequential Mining Method based on a New Criterion, Proc. of the Artificial Intelligence and Soft Computing 2006, pp.1-8, 2006.
[11] S. Sakurai, Y. Kitahara, R. Orihara, K. Iwata, N. Honda, and T. Hayashi, Discovery of Sequential Patterns Coinciding with Analysts’ Interests, J. of Computers, vol.3, no.7, pp.1-8, 2008.
[12] S. Sakurai, An Efficient Discovery Method of Patterns from Transactions with their Classes, Proc. of the 2010 IEEE Intl. Conf. on Systems, Man and Cybernetics, pp.2116-2123, 2010.
[13] Y. -W. Seo, J. A. Giampapa, and K. P. Sycaratech, Financial News Analysis for Intelligent Portfolio Management, Report CMU-RI-TR-04-04, Robotics Institute, Carnegie Mellon University, January, 2004.
[14] X. Zhang, H. Fuehres, and P. A. Gloor, Predicting Stock Market Indicators through Twitter "I hope it is not as bad as I fear”, Procedia - Social and Behavioral Sciences, vol.26, pp.55-62, 2011.
[15] http://www.geocities.jp/sundaysoftware/csv/keiretu.html
[16] http://www11.ocn.ne.jp/˜ kui168/link37.html
[17] http://www11.ocn.ne.jp/˜ kui168/link39.html