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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.7, No.2, Jan. 2015

Time Series Forecasting Model Based on Discrete Grey LS-SVM

Full Text (PDF, 403KB), PP.27-33


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Author(s)

De-qiang Zhou

Index Terms

Time series prediction, Least square support vector machines algorithm, Grey system, Small samples, Discrete GM(1,1) model, Discrete grey least squares support vector machine

Abstract

The advantages and disadvantages of discrete GM(1,1) model and least squares support vector machine are analyzed respectively, this article proposes a new time series forecasting model of discrete grey least squares support vector machine. The new model adopts structural risk minimization principle, at the same time develops the advantages of accumulation generation in the grey forecasting method, weakens the effect of stochastic-disturbing factors in original sequence, and avoids the theoretical defects existing in the grey forecasting model. The simulation results show that the forecasting model is effective and reliable, and consolidates the advantage of the discrete GM(1,1) model and least squares support vector machine. It offers a new way to improve the time series forecasting accuracy.

Cite This Paper

De-qiang Zhou,"Time Series Forecasting Model Based on Discrete Grey LS-SVM", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.2, pp.27-33, 2015. DOI: 10.5815/ijisa.2015.02.04

Reference

[1]Kayacan. E, Ulutas. B, & Kaynak. O, “ Grey system theory-based models in time series prediction”, Expert Systems with Applications, Vol. 33, No. 2, pp. 1784-1789, Mar. 2010.

[2]Julong, Deng,. “Contral problems of grey system”, Systems& Contral Letters, Vol. 1,No.5, pp. 288- 294, Mar.1982.

[3]Bo Zeng, Sifeng Liu, and Naiming Xie, “Prediction model of interval grey number based on DGM(1,1)”,Journal of Systems Engineering and Electronics, Vol.21,No.4, pp.598-603,Aug.2010.

[4]Dahai Zhang, Sifang Wang, and KaiQuang Shi, “Theoretical Defect of Grey Prediction Formula and Its Improvement”, Systems Engineering-Theory&Practice,, Vol.22,N0.8,pp.140-142, Aug.2002.

[5]Guanjun Tan, “The Structure Method and Application of Background Value in Grey System GM(1,1)Model(I)”,Systems Engineering-Theory & Practice,Vol.20 N0.4,pp.98-103.Apr. 2000.

[6]Deqiang Zhou, “GM(1,1)model based on least absolute deviation and application in the power load forecasting”,Power System Protection and Control, Vol.39,No.1,pp.100-103, Jan,2011.

[7]Naiming Xie, and Sifeng Liu, “Discrete GM(1, 1) and mechanism of grey forecasting model”, Systems Engineering-Theory & Practice, Vol.25,No. 1,pp. 93–99, Jan,2005.

[8]Zhaoning Zheng, Deshun Liu, “Direct Modeling Improved GM (1, 1) Model IGM (1, 1) by Genetic Algorithm”, Systems Engineering Theory & Practice, Vol.23,No.5,pp. 99-102, May,2003.

[9]Vapnik. V. N, The nature of statistical learning theory, Springer -Verlag,New York, 1995.

[10]Dehong An, Wenxiu Han,and Yihong Yue, “Improved combination forecast method and its application in short-term load forceasting of a power system”,Journal of Systems Engineering and Electronics, Vol. 26, No. 6, pp. 842-844,Jun.2006.

[11]Hung. Y.H, and Liao. Y.S, “Application PCA and Fixed Size LS-SVM Method for Large Scale Classification Problems”, Information Technology Journal, Vol.7,No.6,pp. 890-896,Jun.2008.

[12]Wu. F.F,and Zhao. Y.L, “Least Square Support Vector Machine on Molet Wavelet Kernel Function and its Application to Nonlinear System Identification”, Information Technology Journal, Vol.5,No.3,pp.439-444, Mar.2006.

[13]Suykens. J. A. K, and Vandewalle. J, “Least squares support vector machine classfiers”, Neural Processing Letters, Vol.9, No.3,pp.293-300, Jun.1999.

[14]Wang.L. J,Lai.H. C,and Zhang.T. Y, “An Improved on Least Square Support Vector Machines”, Information Technology Journal, Vol.7,N0.2, pp.370-373, Feb.2008.

[15]Yatong Zhou, Taiyi Zhang, and Liejun Wang, “On the Relationship between LS-SVM, MSA, and LSA”, International Journal of Computer Science and Network Security, Vol.6 No.11, pp. 01-05, Nov.2006.

[16]C. C. Chiang, M. C. Ho, and J. A, Chen. “A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting”, Neural Computing & Applications, Vol.15,No.3,pp.328–338, Mar,2006.

[17]John Paul T. Yusiong, “Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm”, International Journal of Intelligent Systems and Applications, Vol.5,No.1,pp. 69-80, Dec. 2012.

[18]K. Y., Huang, C. J. Jane. “A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories”, Expert Systems with Applications, Vol.36,No.3,pp. 5387-5392, 2009.

[19]B. R. Chang, H. F. Tsai. “Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity”, Expert Systems with Applications, Vol.34,pp.925–934, 2008.

[20]Qiang Song, and Ai-min Wang, “Simulation and Prediction of Alkalinity in Sintering ProcessBased on Grey Least Squares Support Vector Machine”, Journal of Iron and Steel Research, International, Vol.16, No.5,pp.01-06, Sept.2009.

[21]Xuemei Li, Ming Shao,and Lixing Ding, “Particle Swarm Optimization-based LS-SVM for Building Cooling Load Prediction”, Journal of Computers, Vol. 5, No. 4, pp.614-621,Apr. 2010.

[22]Popp R L, Pattipati K R, “Bar-Shalom Y. m-Best S-D assignment algorithm with application to multitarget tracking”, IEEE Trans. on AC, Vol.37,No.1,pp.22 – 38, Jan,2001.