Abstract
Several studies have applied genetic programming (GP) to the task of forecasting with favourable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new “dynamic” GP model that is specifically tailored for forecasting in non-static environments. This Dynamic Forecasting Genetic Program (DyFor GP) model incorporates methods to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is realised and tested for forecasting efficacy on real-world economic time series, namely the U.S. Gross Domestic Product and Consumer Price Index Inflation. Results show that the DyFor GP model outperforms benchmark models from leading studies for both experiments. These findings affirm the DyFor GP’s potential as an adaptive, non-linear model for real-world forecasting applications and suggest further investigations.
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References
Angeline, P.: Genetic programming and emergent intelligence. Advances in Genetic Programming 1, 75–98 (1994)
Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5, 17–26 (2001)
Iba, H., Nikolaev, N.: Genetic programming polynomial models of financial data series. In: Proceedings of the 2000 Congress of Evolutionary Computation, vol. 1, pp. 1459–1466 (2000)
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)
Kaboudan, M.: Forecasting with computer-evolved model specifications: a genetic programming application. Computer and Operations Research 30, 1661–1681 (2003)
Kitchen, J., Monaco, R.: Real-time forecasting in practice. Business Economics: the Journal of the National Association of Business Economists 38, 10–19 (2003)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting: methods and applications. John Wiley and Sons, Inc., Chichester (1998)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)
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, vol. 1, pp. 243–253 (1999)
Smith, K., Gupta, J.: Neural Networks in Business: Techniques and Applications. Idea Group Pub. (2002)
Stock, J., Watson, M.: Forecasting inflation. Journal of Monetary Economics 44, 293–335 (1999)
Wagner, N., Michalewicz, Z., Khouja, M., McGregor, R.: Time series forecasting for dynamic environments: the DyFor genetic program model. Submitted to IEEE Transactions on Evolutionary Computation (2005)
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Wagner, N., Michalewicz, Z., Khouja, M., McGregor, R.R. (2005). Forecasting with a Dynamic Window of Time:The DyFor Genetic Program Model. In: Bolc, L., Michalewicz, Z., Nishida, T. (eds) Intelligent Media Technology for Communicative Intelligence. IMTCI 2004. Lecture Notes in Computer Science(), vol 3490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558637_21
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DOI: https://doi.org/10.1007/11558637_21
Publisher Name: Springer, Berlin, Heidelberg
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