Abstract
Two novel methods for Time Series Prediction based on GEP (Gene Expression Programming). The main contributions include: (1) GEP-Sliding Window Prediction Method (GEP-SWPM) to mine the relationship between future and historical data directly. (2) GEP-Differential Equation Prediction Method (GEP-DEPM) to mine ordinary differential equations from training data, and predict future trends based on specified initial conditions. (3) A brand new equation mining method, called Differential by Microscope Interpolation (DMI) that boosts the efficiency of our methods. (4) A new, simple and effective GEP-constants generation method called Meta-Constants (MC) is proposed. (5) It is proved that a minimum expression discovered by GEP-MC method with error not exceeding δ/2 uses at most log3(2L/δ) operators and the problem to find δ-accurate expression with fewer operators is NP-hard. Extensive experiments on real data sets for sun spot prediction show that the performance of the new method is 20-900 times higher than existing algorithms.
Supported by the National Science Foundation of China Grant #60073046, Specialized Research Fund for the Doctoral Program of Higher Education SRFDP #20020610007, The National Science Foundation of Guangxi Grant #0339039, and National Basic Research 973 Program of China 2002CB111504.
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References
Han, J., Gong, W., Yin, Y.: Mining Segment-wise Periodic Pattern in Time Related Databases. In: Proc. Of 1998 of International Conf. On Knowledge Discovery and Data Mining (KDD 1998), New York City, NY (August 1998)
Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proc. Of 1998 international Conference Data Engineering ICDE 1998, pp. 412–421 (1998)
Xiang, J.T., Du, J.Q., Shi, J.: Dynamic Data Processing: Time Series Analysis. Meteorology Press (1988)
Renchu, G.: The Statistical Analysis of Dynamic Data. Bejing University of Science and Technology Press (1991)
Ferreira, C.: Gene Expression Programming: A new adaptive Algorithm for solving Problems. Complex Systems 13, 87–129 (2001)
Jie, Z., Changjie, T., Tianqing, Z.: Mining Predicate Association Rule by Gene Expression Programming. In: Meng, X., Su, J., Wang, Y. (eds.) WAIM 2002. LNCS, vol. 2419, pp. 92–103. Springer, Heidelberg (2002)
George, G.S.: Forecasting Chaotic Time Series with Genetic Algotithms. Physical Review E 55(3), 2557–2567 (1997)
Kang, L.S., Cao, H.Q., Chen, Y.: The Evolutionary Modeling Algorithm for System of Odinary Differential Equations. Chinese Journal of Computer 22(8), 871–876 (1999)
Hongqing, C., Lishan, K., Yuping, C.: The Evolutionary Modeling of Higher- Order Odinary Differential Equaions for Time Series Analysis. Mini-Micro System 21 (2000)
Minqiang, L., Jisong, K.: The Theory and Application of Genetic Algorithm. Science Press ISBN 7-03-009960-5/C.67
Sipser, M.: Introdoction to the theory of computing. PWS publishing company, a division of Leaning (1997)
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Zuo, J., Tang, Cj., Li, C., Yuan, Ca., Chen, Al. (2004). Time Series Prediction Based on Gene Expression Programming. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_7
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DOI: https://doi.org/10.1007/978-3-540-27772-9_7
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