Abstract
In this paper, we develop an efficient system to detect outliers from real-life financial time series comprising of security prices. Our system consists of a data mining algorithm and a statistical algorithm. When applying each of these two algorithms individually, we observed its strengths and weaknesses. To overcome the weaknesses of the two algorithms, we combine the algorithms together. By so doing, we efficiently detect outliers from the financial time series. Moreover, the resulting (processed) datasets can then be used as input for some financial models used in forecasting future security prices or in predicting future market behaviour. This shows an alternative role of our outlier detection system—serving as a pre-processing step for other financial models.
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References
Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., Swami, A.: An interval classifier for database mining applications. In: Proc. VLDB 1992, pp. 560–573 (1992)
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, ch. 12 (1996)
Back, A.D., Weigend, A.S.: A first application of independent component analysis to extracting structure from stock returns. World Scientific - IJNS 8, 473–484 (1997)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. KDD 1996, pp. 226–231 (1996)
Jolliffe, I.: Principal Component Analysis. Springer, Heidelberg (1986)
Keogh, E., Lonardi, S., Chiu, B.Y.: Finding surprising patterns in a time series database in linear time and space. In: Proc. KDD 2002, pp. 550–556 (2002)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Proc. VLDB 1998, pp. 392–403 (1998)
Lakshmanan, L.V.S., Leung, C.K.-S., Ng, R.T.: Dynamic mining of constrained frequent sets. ACM TODS 28, 337–389 (2003)
Leung, C.K.-S.: Evaluation of data mining opportunities at Workers Compensation Board. Research report, Workers Compensation Board of BC, Canada (1998)
Leung, C.K.-S., Khan, Q.I., Hoque, T.: CanTree: a tree structure for efficient incremental mining of frequent patterns. In: Proc. ICDM 2005, pp. 274–281 (2005)
Omiecinski, E., Savasere, A.: Efficient mining of association rules in large dynamic databases. In: Embury, S.M., Fiddian, N.J., Gray, W.A., Jones, A.C. (eds.) BNCOD 1998. LNCS, vol. 1405, pp. 49–63. Springer, Heidelberg (1998)
Park, J.: Modern portfolio theory and its application to hedge funds: Part II. MFA Reporter 1–2, 4, 12–13 (2001)
Schwertman, N.C., Owens, M.A., Adnan, R.: A simple more general boxplot method for identifying outliers. Elsevier - CSDA 47, 165–174 (2004)
Srikumar, K., Bhasker, B., Tripathi, S.K.: MaxDomino: efficiently mining maximal sets. In: James, A., Younas, M., Lings, B. (eds.) BNCOD 2003. LNCS, vol. 2712, pp. 131–139. Springer, Heidelberg (2003)
Utans, J.W., Holt, T., Refenes, A.N.: Principal components for modelling multi-currency portfolios. In: Proc. NNCM 1996, pp. 359–368 (1996)
Victoria-Feser, M.-P.: Robust portfolio selection. Working paper 2000.14, University of Geneva, Switzerland (2000)
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Leung, C.KS., Thulasiram, R.K., Bondarenko, D.A. (2006). An Efficient System for Detecting Outliers from Financial Time Series. In: Bell, D.A., Hong, J. (eds) Flexible and Efficient Information Handling. BNCOD 2006. Lecture Notes in Computer Science, vol 4042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788911_16
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DOI: https://doi.org/10.1007/11788911_16
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