Summary
Detection of turning points is a critical task for financial forecasting applications. This chapter proposes a comparison between two different classification approaches on such a problem. Nature-Inspired methodologies are attracting growing interest due to their ability to cope with complex tasks like classification, forecasting, and anomaly detection problems. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), and an artificial immune system algorithm, namely Negative Selection (NS), have been applied to the task of detecting turning points, modeled as an Anomaly Detection (AD) problem. Particular attention has also been given to the choice of the features considered as inputs to the classifiers, due to the significant impact they may have on the overall accuracy of the approach. In this work, starting from a set of eight input features, feature selection has been carried out by means of a greedy hill climbing algorithm, in order to analyze the incidence of feature reduction on the global accuracy of the approach. The performances obtained from the two approaches have also been compared to other traditional machine learning techniques implemented by WEKA and both methods have been found to give interesting results with respect to traditional techniques.
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References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. In: Machine Learning, pp. 37–66 (1991)
Azzini, A., De Felice, M., Meloni, S., Tettamanzi, A.G.B.: Soft computing techniques for internet backbone traffic anomaly detection. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 99–104. Springer, Heidelberg (2009)
Azzini, A., De Felice, M., Tettamanzi, A.G.B.: A study of nature-inspired methods for financial trend reversal detection. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 161–170. Springer, Heidelberg (2010)
Balachandran, S., Dasgupta, D., Nino, F., Garrett, D.: A framework for evolving multi-shaped detectors in negative selection. In: Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, pp. 401–408 (2007)
Bishop, C.M.: Neural Networks for Pattern Recognition, 1st edn. Oxford University Press, Oxford (1996), http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0198538642
Caruana, R., Freitag, D.: Greedy attribute selection. In: Proceedings of the Eleventh International Conference on Machine Learning, pp. 28–36. Morgan Kaufmann, San Francisco (1994)
Chang, J.F., Hsu, S.W.: The construction of stock’s portfolios by using particle swarm optimization. In: ICICIC 2007: Proceedings of the Second International Conference on Innovative Computing, Information and Control, p. 390. IEEE Computer Society, Washington, DC, USA (2007), http://dx.doi.org/10.1109/ICICIC.2007.568
Cleary, J.G., Trigg, L.E.: K*: An instance-based learner using an entropic distance measure. In: Proceedings of the 12th International Conference on Machine Learning, pp. 108–114. Morgan Kaufmann, San Francisco (1995)
Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Francisco (1995)
Colby, R.W., Meyers, T.A.: The Encyclopedia Of Technical Market Indicators. McGraw-Hill, New York (1988)
Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2003)
Falco, I.D., Cioppa, A.D., Tarantino, E.: Facing classification problems with particle swarm optimization. Applied Soft Computing 7(3), 652–658 (2007), http://www.sciencedirect.com/science/article/B6W86-4J61657-1/2/1789a219e3a464b9c22947693cca47c8 , doi:10.1016/j.asoc.2005.09.004
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, pp. 202–212. IEEE Computer Society Press, Los Alamitos (1994)
Freund, Y.: The alternating decision tree learning algorithm. In: Machine Learning: Proceedings of the Sixteenth International Conference, pp. 124–133. Morgan Kaufmann, San Francisco (1999)
Glickman, M., Balthrop, J., Forrest, S.: A machine learning evaluation of an artificial immune system. Journal of Evolutionary Computation 13(2), 179–212 (2005)
Gonzalez, F., Dasgupta, D., Kozma, R.: Combining negative selection and classification techniques for anomaly detection. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 705–710 (2002)
González, F., Dasgupta, D., Niño, L.: A randomized real-valued negative selection algorithm. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 261–272. Springer, Heidelberg (2003), http://dx.doi.org/10.1007/978-3-540-45192-1_25
Herbst, A.: Analyzing and Forecasting Futures Prices. Wiley, Chichester (1992)
Jang, G.S., Lai, F., Jiang, B.W., Chien, L.H.: An intelligent trend prediction and reversal recognition system using dual-module neural networks. In: Proceedings of the First International Conference on Artificial Intelligence on Wall Street, pp. 42–51 (1991), doi:10.1109/AIAWS.1991.236575
Ji, Z., Dasgupta, D.: Applicability issues of the real-valued negative selection algorithms. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 111–118. ACM Press, New York (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995), doi:10.1109/ICNN.1995.488968
Jae Kim, K., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications 19(2), 125–132 (2000), http://www.sciencedirect.com/science/article/B6V03-40P8SC5-5/2/b16d2b8805a754de8214b5d60b666481 , doi:10.1016/S0957-4174(00)00027-0
La, T., de Oliveira, A.: Predicting stock trends through technical analysis and nearest neighbor classification. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2009), pp. 3094–3099 (2009), doi:10.1109/ICSMC.2009.5345944
Middlemiss, M.: Positive and negative selection in a multilayer artificial immune system. Tech. Rep. 2006/03, Department of Information Science, Otago (2006)
Murphy, J.: Technical Analysis of the Financial Markets. New York Institute of Finance (1999)
Peters, E.: Chaos and Order in the Capital Markets, 2nd edn. Wiley, New York (1996)
Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. App. 2008, 1–10 (2008), http://dx.doi.org/10.1155/2008/685175
Pudil, P., Novovicov, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15(11), 1119–1125 (1994), http://www.sciencedirect.com/science/article/B6V15-48MPBV9-7/2/c88897a586e12c1a46510ac12fd6c27d , doi:10.1016/0167-8655(94)90127-9
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Rennard, J.P.: Handbook of Research on Nature-inspired Computing for Economics and Management. IGI Publishing, Hershey (2006)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics 21(3), 660–674 (1991), http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=97458
Sewell, M.: Feature selection (2007)
Sousa, T., Silva, A., Neves, A.: Particle swarm based data mining algorithms for classification tasks. Parallel Computing 30(5-6), 767–783 (2004), doi:10.1016/j.parco.2003.12.015
Stibor, T., Mohr, P., Timmis, J.: Is negative selection appropriate for anomaly detection? In: Proceedings of the International Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 321–328 (2005)
Tax, D.: One-class classification. PhD thesis, TU Delft, Delft University of Technology (2001)
Vanstone, B., Tan, C.: A survey of the application of soft computing to investment and financial trading. In: Australian and New Zeland Intelligent Information Systems Conference, pp. 211–216 (2003)
Vince, R.: The Handbook of Portfolio Mathematics: Formulas for optimal allocation & leverage. Wiley, Chichester (2007)
Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations. SIGMOD Rec. 31(1), 76–77 (2002), http://doi.acm.org/10.1145/507338.507355
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005), http://www.worldcat.org/isbn/0120884070
Ye, Q., Liang, B., Li, Y.: Amnestic neural network for classification: application on stock trend prediction. In: Proceedings of International Conference on Services Systems and Services Management, 2005 (ICSSSM 2005), vol. 2, pp. 1031–1034 (2005), doi:10.1109/ICSSSM.2005.1500149
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Azzini, A., De Felice, M., Tettamanzi, A.G.B. (2011). A Comparison between Nature-Inspired and Machine Learning Approaches to Detecting Trend Reversals in Financial Time Series. In: Brabazon, A., O’Neill, M., Maringer, D. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23336-4_3
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