Abstract
Evolutionary Computation techniques have proven their applicability for time series forecasting in a number of studies. However these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world forecasting concerns which are characterized by uncertain environments and constantly-shifting conditions. This chapter summarizes the results of recent studies that investigate adaptive evolutionary techniques for time series forecasting in non-static environments and proposes a new, self-adaptive technique that addresses shortcomings seen from these studies. A theoretical analysis of the proposed technique’s efficacy in the presence of shifting conditions and noise is given.
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References
Andreou, A., Georgopoulos, E., Likothanassis, S.: Exchange rates forecasting: a hybrid algorithm based on genetically optimized adaptive neural networks. Computational Economics 20, 191–202 (2002)
Andrew, M., Prager, R.: Genetic programming for the acquistion of double auction market strategies. In: Kinnear, K. (ed.) Advances in Genetic Programming, pp. 355–368. MIT Press, Cambridge (1994)
Back, B., Laitinen, T., Sere, K.: Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications 11, 407–413 (1996)
Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, and Genetic Algorithms. Oxford University Press, Oxford (1996)
Chambers, L. (ed.): Practical Handbook of Genetic Algorithms: Applications. CRC Press, Boca Raton (1995)
Chen, S., Yeh, C.: Toward a computable approach to the efficient market hypothesis: an application of genetic programming. Journal of Economics Dynamics and Control 21, 1043–1063 (1996)
Chen, S., Yeh, C., Lee, W.: Option pricing with genetic programming. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 32–37 (1998)
Chen, Y., Yang, B., Abraham, A.: Time series forecasting using flexible neural tree model. Information Sciences 174, 219–235 (2005)
Chen, Y., Yang, B., Abraham, A.: Flexible neural trees ensemble for stock index modeling. Neurocomputing Journal 70, 305–313 (2007)
Chiraphadhanakul, S., Dangprasert, P., Avatchanakorn, V.: Genetic algorithms in forecasting commercial banks deposit. In: Proceedings of the IEEE International Conference on Intelligent Processing Systems, pp. 557–565 (1997)
Deboeck, G. (ed.): Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic and Financial Markets. John Wiley and Sons, Inc., Chichester (1994)
Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (1999)
Fogel, D., Chellapilla, K.: Revisiting evolutionary programming. In: SPIE Aerosense 1998, Applications and Science of Computational Intelligence, pp. 2–11 (1998)
Fogel, L., Angeline, P., Fogel, D.: An evolutionary programming approach to self-adaptation on finite state machines. In: Proceedings of the 4th Annual Conference on Evolutionary Programming, pp. 355–365 (1995)
Fogel, L., Owens, A., Walsh, M.: Artificial Intelligence through Simulated Evolution. Wiley, Inc., Chichester (1966)
Gately, E.: Neural Networks for Financial Forecasting. John Wiley and Sons, Inc., Chichester (1996)
Goto, Y., Yukita, K., Mizuno, K., Ichiyanagi, K.: Daily peak load forecasting by structured representation on genetic algorithms for function fitting. Transactions of the Institute of Electrical Engineers of Japan 119, 735–736 (1999)
Gurney, K.: An Introduction to Neural Networks. UCL Press (1997)
Hiden, H., McKay, B., Willis, M., Tham, M.: Non-linear partial least squares using gentic programming. In: Genetic Programming 1998: Proceedings of the Third Annual Conference, pp. 128–133 (1998)
Iba, H., Nikolaev, N.: Genetic programming polynomial models of financial data series. In: Proceedings of the 2000 Congress of Evolutionary Computation, pp. 1459–1466. IEEE, Los Alamitos (2000)
Iba, H., Sasaki, T.: Using genetic programming to predict financial data. In: Proceedings of the Congress of Evolutionary Computation, pp. 244–251 (1999)
Jeong, B., Jung, H., Park, N.: A computerized causal forecasting system using genetic algorithms in supply chain management. The Journal of Systems and Software 60, 223–237 (2002)
Jonsson, P., Barklund, J.: Characterizing signal behavior using genetic programming. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 62–72. Springer, Heidelberg (1996)
Ju, Y., Kim, C., Shim, J.: Genetic based fuzzy models: interest rate forecasting problem. Computers and Industrial Engineering 33, 561–564 (1997)
Kaboudan, M.: Forecasting stock returns using genetic programming in c++. In: Proceedings of 11th Annual Florida Artificial Intelligence International Research Symposium, pp. 502–511 (1998)
Kaboudan, M.: Genetic evolution of regression models for business and economic forecasting. In: Proceedings of the 1999 Congress of Evolutionary Computation, pp. 1260–1268. IEEE, Los Alamitos (1999)
Kaboudan, M.: Genetic programming prediction of stock prices. Computational Economics 6, 207–236 (2000)
Kaboudan, M.: Genetically evolved models and normality of their residuals. Journal of Economics Dynamics and Control 25, 1719–1749 (2001)
Kaboudan, M.: Forecasting with computer-evolved model specifications: a genetic programming application. Computer and Operations Research 30, 1661–1681 (2003)
Kim, D., Kim, C.: Forecasting time series with genetic fuzzy predictor ensemble. IEEE Transactions on Fuzzy Systems 5, 523–535 (1997)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Lee, D., Lee, B., Chang, S.: Genetic programming model for long-term forecasting of electric power demand. Electric Power Systems Research 40, 17–22 (1997)
Leigh, W., Purvis, R., Ragusa, J.: Forecasting the nyse composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support. Decision Support Systems 32, 361–377 (2002)
Liu, Y., Yao, X.: Evolving neural networks for hang seng stock index forecasting. In: CECCO 2001: Proceedings of the 2001 Congress on Evolutionary Computation, pp. 256–260 (2001)
Maniezzo, V.: Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks 5, 39–53 (1994)
Maqsood, I., Abraham, A.: Weather analysis using and ensemble of connectionist learning paradigms. Applied Soft Computing Journal 7, 995–1004 (2007)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Mulloy, B., Riolo, R., Savit, R.: Dynamics of genetic programming and chaotic time series prediction. In: Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 166–174 (1996)
Nag, A., Mitra, A.: Forecasting daily foreign exhange rates using genetically optimized neural networks. Journal of Forecasting 21, 501–511 (2002)
Neely, C., Weller, P.: Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics. Technical report, The Federal Reserve Bank of St. Louis (2002)
Phua, P., Ming, D., Lin, W.: Neural network with genetically evolved algorithms for stocks prediction. Asia-Pacific Journal of Operational Research 18, 103–107 (2001)
Sathyanarayan, R., Birru, S., Chellapilla, K.: Evolving nonlinear time series models using evolutionary programming. In: CECCO 1999: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 243–253 (1999)
Sexton, R.: Identifying irrelevant input variables in chaotic time series problems: using genetic algorithms for training neural networks. Journal of Computational Intelligence in Finance 6, 34–41 (1998)
Smith, K., Gupta, J.: Neural Networks in Business: Techniques and Applications. Idea Group Pub. (2002)
Trippi, R., Turban, E. (eds.): Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. Irwin Professional Pub. (1996)
Venkatesan, R., Kumar, V.: A genetic algorithms approach to growth phase forecasting of wireless subscribers. International Journal of Forecasting 18, 625–646 (2002)
Wagner, N., Michalewicz, Z.: Genetic programming with efficient population control for financial times series prediction. In: 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, pp. 458–462 (2001)
Wagner, N., Michalewicz, Z.: Forecasting with a dynamic window of time: the dyfor genetic program model. In: Bolc, L., Michalewicz, Z., Nishida, T. (eds.) IMTCI 2004. LNCS, vol. 3490, pp. 205–215. Springer, Heidelberg (2005)
Wagner, N., Michalewicz, Z.: An analysis of adaptive windowing for time series forecasting in dynamic environments: Further tests of the DyFor GP model. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2008), Atlanta, GA (July 2008)
Wagner, N., Michalewicz, Z., Khouja, M., McGregor, R.: Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Transactions on Evolutionary Computation 11(4), 433–452 (2007)
White, H.: Artificial neural networks: approximation and learning theory. Blackwell, Malden (1992)
White, J.: A genetic adaptive neural network approach to pricing option: a simulation analysis. Journal of Computational Intelligence in Finance 6, 13–23 (1998)
Yao, X., Liu, Y.: Epnet for chaotic time series prediction. In: First Asia-Pacific Complex Systems Conference, pp. 146–156 (1997)
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Wagner, N., Michalewicz, Z. (2009). Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_1
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