Time Series Perturbation Modeling Algorithm - Combination of Genetic Programming and Quantum Mechanical Perturbation Theory


The KIPS Transactions:PartB , Vol. 9, No. 3, pp. 277-286, Jun. 2002
10.3745/KIPSTB.2002.9.3.277,   PDF Download:

Abstract

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to orginial time series to get the first mathematical model. Numerical values of the model are subtracted from the orginal time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.


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Cite this article
[IEEE Style]
G. Y. Lee, "Time Series Perturbation Modeling Algorithm - Combination of Genetic Programming and Quantum Mechanical Perturbation Theory," The KIPS Transactions:PartB , vol. 9, no. 3, pp. 277-286, 2002. DOI: 10.3745/KIPSTB.2002.9.3.277.

[ACM Style]
Geum Yong Lee. 2002. Time Series Perturbation Modeling Algorithm - Combination of Genetic Programming and Quantum Mechanical Perturbation Theory. The KIPS Transactions:PartB , 9, 3, (2002), 277-286. DOI: 10.3745/KIPSTB.2002.9.3.277.