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Application of multiobjective pptimization for time series groups forecasting

Astakhova Nadezhda

graduate student, Department of Computational and Applied Mathematics, Ryazan State Radio Engineering University

390005, Russia, Ryazanskaya oblast', g. Ryazan', ul. Gagarina, 59/1

asnadya@yandex.ru
Demidova Liliya

Doctor of Technical Science

Professor, Department of Computational and Applied Mathematics, Ryazan State Radio Engineering University; Department of Informatics and Automation, Oskovsky Institute of Technology

390005, Russia, Ryazanskaya oblast', g. Ryazan', ul. Gagarina, 59/1

liliya.demidova@rambler.ru
Nikulchev Evgeny

Doctor of Technical Science

Professor, Vice-Rector for Research,  Moscow Institute of Technology; Chief Researcher, Kuban State University

119334, Russia, Moskva, g. Moscow, ul. Leninskii Prospekt, 38A

nikulchev@mail.ru

DOI:

10.7256/2306-4196.2016.5.20414

Review date:

16-09-2016


Publish date:

29-01-2017


Abstract: In article the approach to forecasting of time series groups with use of technologies of the cluster analysis and the principles of multiobjective optimization has been offered. The description of time series – centroids of clusters with use of the forecasting models on the base of the strictly binary trees and the multiobjective modified clonal selection algorithm in case of which the implementation of two quality indicators of models – the affinity indicator based on calculation of an average forecasting relative error and the tendencies discrepancy indicator are involved in selection process of the best forecasting models has been developed. Accounting of quality two indicators of the forecasting model is realized with use of the Pareto-dominance principles applied when forming new populations of the forecasting models in the multiobjective modified clonal selection algorithm. Within the solution of a problem of multiobjective optimization when forming new population of the forecasting models for maintenance of its high variety it is offered to consider values of the crowding distance of the forecasting models. Prospects of application of the general forecasting models created on the base of the strictly binary trees for forecasting of the time series entering one cluster are shown. The results of experimental studies confirming the efficiency of the offered approach to short-term and mid-term forecasting of time series groups within the solution of a problem of multiobjective optimization are given.


Keywords: tendencies discrepancy indicator, affinity indicator, multiobjective optimization, clonal selection algorithm, strictly binary tree, forecasting model, time series, average error rate, Pareto-dominance, crowding distance
This article written in Russian. You can find full text of article in Russian here .

References
1.
Andersen T. Statisticheskii analiz vremennykh ryadov. Moskva: Mir. 1976. 756 s.
2.
Belov V.V. Problemy faktornogo prognozirovaniya sotsial'no-ekonomicheskikh pokazatelei // Vestnik Moskovskogo gosudarstvennogo universiteta priborostroeniya i informatiki. 2005. № 2. S. 116–122.
3.
Terekhov A.A. Identifikatsiya statisticheskogo materiala i konsolidatsiya vremennykh ryadov // Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta. 2009. № 27. S. 62–70.
4.
Petrushin V.N., Rytikov G.O. Formalizatsiya vremennogo ryada metodom dvoinogo sglazhivaniya // Cloud of Science. 2014. T. 1.№ 2. S. 230–238.
5.
Demidova L. A. Razrabotka odnofaktornykh nechetkikh modelei dlya analiza tendentsii vremennykh ryadov s ispol'zovaniem geneticheskogo algoritma // Nauchno-tekhnicheskie vedomosti SPbGPU. 2007. № 52-2. S. 156–164.
6.
Demidova L.A. Genetic Algorithm For Optimal Parameters Search In The One-Factor Forecasting Model Based On Continuous Type-2 Fuzzy Sets // Automation and Remote Control. 2013. T. 74. № 2. S. 313–320.
7.
Paklin N.B. Biznes-analitika ot dannykh k znaniyam / Paklin N.B., Oreshkov V.I. Sankt-Peterburg: Piter, 2013. 704 s.
8.
Chubukova I.A. Data Mining: ucheb. posobie. M.: Internet-universitet informatsionnykh tekhnologii: BINOM: Laboratoriya znanii. 2006. 382 s.
9.
Demidova L.A., Koryachko A.V., Skvortsova T.S. Modifitsirovannyi algoritm klonal'nogo otbora dlya analiza vremennykh ryadov s korotkoi dlinoi aktual'noi chasti // Sistemy upravleniya i informatsionnye tekhnologii. 2010. T. 42. № 4.1. S. 131–136.
10.
Demidova L. A. Modeli prognozirovaniya vremennykh ryadov s korotkoi aktual'noi chast'yu na osnove modifitsirovannogo algoritma klonal'nogo otbora // Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta. 2012. № 39-2. S. 64–71.
11.
Demidova L.A. Otsenka kachestva modelei prognozirovaniya na osnove strogo binarnykh derev'ev i modifitsirovannogo algoritma klonal'nogo otbora // Cloud of Science. 2014. T. 1. № 2. S. 202–222.
12.
Demidova L.A. Time series forecasting models on the base of modified clonal selection algorithm // V sbornike: 2014 International conference on computer technologies in physical and engineering applications (ICCTPEA) Editor: E. I. Veremey. Sankt-Peterburgskii gosudarstvennyi universitet; IEEE (IEEE Catalog number CFP14BDA-USB). 2014. S. 33–34.
13.
Astakhova N.N., Demidova L.A. Metod prognozirovaniya grupp vremennykh ryadov s primeneniem algoritmov klasternogo analiza // Prikaspiiskii zhurnal Upravlenie i vysokie tekhnologii. 2015. № 2. S. 59–79.
14.
Astakhova N.N., Demidova L.A., Nikulchev E.V. Forecasting Method For Grouped Time Series With The Use Of K-Means Algorithm // Applied Mathematical Sciences, Vol. 9, no. 97. 2015, p. 4813–4830.
15.
Astakhova N.N., Demidova L.A., Nikulchev E.V. Forecasting Of Time Series' Groups With Application Of Fuzzy C-Mean Algorithm // Contemporary Engineering Sciences, Vol. 8, no 35. 2015, p. 1659–1677.
16.
Demidova L.A., Astakhova N.N. Mnogotselevaya optimizatsiya dlya modelei prognozirovaniya na osnove strogo binarnykh derev'ev // Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta. 2016. № 55. S. 118-130.
17.
Astakhova N.N., Demidova L.A. Cravnitel'nyi analiz variantov optimizatsii pri razrabotke modelei prognozirovaniya na osnove strogo binarnykh derev'ev // Prikaspiiskii zhurnal: upravlenie i vysokie tekhnologii. 2016. № 2 (34). S. 9–25.
18.
Bentley P.J., Wakefield J.P. Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms // In Proceedings of the 2nd On-Line World Conference on Soft Computing in Engineering Design and Manufacturing. 1997. pp. 126–140.
19.
Branke J. Memory Enchanced Evolutionary Algorithms for Changing Optimization Problems, Institute AIFB, University of Karlsruhe, 1999. pp. 1049–1063.
20.
Coello Coello C.A., Cruz Cortés N. An approach to solve multiobjective optimization problems based on an artificial immune system // Proceedings of the First International Conference on Artificial Immune Systems, University of Kent at Canterbury, UK, September 9–11. 2002. pp. 212–221.
21.
Campelo, F., Guimarães, F. G., Saldanha, R. R., Igarashi, H., Noguchi, S., Lowther, D. A., & Ramirez, J. A. A novel multiobjective immune algorithm using nondominated sorting. In 11th International // IGTE Symposium on Numerical Field Calculation in Electrical Engineering. 2004.
22.
Deb K., Pratap A., Agarwal S., Meyarivan T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA II // KanGAL Report No. 200001. Indian Institute of Technology. Kanpur, India. 2000. pp. 182–197.
23.
Deb K., Jain H. An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation. 2014. Vol. 18(4). pp. 577–601.
24.
Deb K. Multiobjective Optimization using Evolutionary Algorithms. Chichester. UK: Wiley. 2001. pp. 221–232.
25.
Eberhart R.C., Kennedy J. A new optimizer using particle swarm theory //Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Piscataway, NJ: IEEE Service Center. 1995. pp. 39-43.
26.
Fonseca C.M., Fleming P.J. Multiobjective optimization and multiple constraint han-dling with evolutionary algorithms-Part I: A unified formulation // Technical report 564. University of Sheffield, Sheffield. UK. January. 1995. pp. 1–16.
27.
Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning // Reading. Massachusetts: Addison-Wesley. 1989. 372 p.
28.
Jiao L., Gong M., Shang R., Du H., Lu B. Clonal selection with immune dominance and anergy based multiobjective optimization // 3rd International Conference on Evolutionary Multi-Criterion Optimization. 2005. pp. 474–489.
29.
Jiao L., Gong M., Du H., Bo L. Multiobjective immune algorithm with nondominated neighbor-based selection // Evolutionary Computation. Vol. 16. Issue 2. Summer. 2008. pp. 225–255.
30.
Horn J., Nafpliotis N., Goldberg D.E. A niched Pareto genetic algorithm for multiobjective optimization // In Proceedings of the First IEEE Conference on Evolutionary Computation. Piscataway. Vol. 1. 1994. pp. 82–87.
31.
Kennedy J., Eberhart R. C. A discrete binary version of the particle swarm algorithm // Proc. 1997 Conf. on Systems, Man, and Cybernetics Piscataway, NJ: IEEE Service Center. 1997. pp. 4104–4109.
32.
Knowles J., Corne D. The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization // Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway. New Jersey: IEEE Service Center. 1999. pp. 98–105.
33.
Luh G.-C., Chueh C.-H., Liu W.-W. MOIA: Multi-Objective Immune Alghorithm //Computers and Structures Vol. 82. 2004. pp. 829–844.
34.
Zitzler E., Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on evolutionary computation. 1999. pp. 257–271.
35.
Srinivas N., Deb K. Multiple-Objective function optimization using non-dominated sorting genetic algorithms //Evolutionary Computation. Vol. 2. 1995. pp. 221–248.
36.
Wang, X. L., & Mahfouf, M. ACSAMO: An Adaptive Multiobjective Optimization Algorithm using the Clonal Selection Principle //2nd European Symposium on Nature-Inspired Smart Information Systems. 2006. pp. 1–12.
37.
Zhang, Z. Constrained Multiobjective Optimization Immune Algorithm: Convergence and Application //Computers and Mathematics with Applications. 2006. Vol. 52(5). pp. 791–808.
38.
Schaffer, J.D. Multiple objective optimization with vector evaluated genetic algorithms // In J. J. Grefenstette (Ed.), Proceedings of an International Conference on Genetic Algorithms and Their Applications, Pittsburgh, PA, 1985. pp. 93–100.
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