ZHANG Shou-peng, LIU Shan, CHAI Wang-xu, ZHANG Jia-qi, GUO Yang-ming. LS-SVR and AGO Based Time Series Prediction Method[J]. International Journal of Plant Engineering and Management, 2016, 21(1): 1-13

LS-SVR and AGO Based Time Series Prediction Method
ZHANG Shou-peng1, LIU Shan2, CHAI Wang-xu2, ZHANG Jia-qi2, GUO Yang-ming2
1 Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an 710068, P. R. China;
2 School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, P. R. China
Recently, fault or health condition prediction of complex systems becomes an interesting research topic. However, it is difficult to establish precise physical model for complex systems, and the time series properties are often necessary to be incorporated for the prediction in practice. Currently, the LS-SVR is widely adopted for prediction of systems with time series data. In this paper, in order to improve the prediction accuracy, accumulated generating operation (AGO) is carried out to improve the data quality and regularity of raw time series data based on grey system theory; then, the inverse accumulated generating operation (IAGO) is performed to obtain the prediction results. In addition, due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR, a modified Gaussian radial basis function (RBF) is proposed. The requirements of distance functions-based kernel functions are satisfied, which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity. The presented model is applied to the analysis of benchmarks. As indicated by the results, the proposed method is an effective prediction one with good precision.
Key words:    time series prediction    least squares support vector regression (LS-SVR)    Gaussian radial basis function (RBF)    accumulated generating operation (AGO)   
Received: 2016-01-20     Revised:
DOI: 10.13434/j.cnki.1007-4546.2016.0101
Funds: This work is supported by National Natural Science Foundation (NNSF) of China under Grant No. 61371024, Aviation Science Fund of China under Grant No. 2013ZD53051, Aerospace Technology Support Fund of China, the Industry-Academy-Research Project of AVIC (cxy2013XGD14).
Corresponding author:     Email:
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ZHANG Shou-peng
LIU Shan
CHAI Wang-xu
ZHANG Jia-qi
GUO Yang-ming

[1] Caesarendra W, Widodo A, et al. Combined probability approach and indirect data-driven method for bearing degradation prognostics[J]. IEEE Transactions on Reliability, 2011,60(1):14-20
[2] Liu D T, Peng Y, Peng X Y. Online adaptive status prediction strategy for data-driven fault prognostics of complex systems[C]//Proceedings of Prognostics and System Health Management Conference, 2011:1-6,24-25 (in Chinese)
[3] Pecht M, Jaai R. A prognostics and health management roadmap for information and electronics-rich systems[J]. IEICE Fundamentals Review, 2010,3(4):25-32
[4] Qu J, Zuo M J. An LSSVR-based algorithm for online system condition prognostics[J]. Expert Systems with Applications, 2012,39(5):6089-6102
[5] Qi M, Zhang G P. Trend time series modeling and forecasting with neural networks[J]. IEEE Transactions on Neural Networks, 2008,19(5):808-816
[6] Valeriy G, Supriya B. Support vector machine as an efficient framework for stock market volatility forecasting[J]. Computational Management Science, 2006,3(2):147-160
[7] Kecman V. Learning and soft computing: support vector machines. neural networks, and fuzzy logic models[M]. Cambridge, MA,USA:MIT Press, 2001
[8] Hansen J V, Nelson R D. Neural networks and traditional time series methods: a synergistic combination in state economic forecast[J]. IEEE transactions on Neural Networks, 1997,8(4):863-873
[9] Vapnik V. The nature of statistical learning theory[M]. New York, USA Springer Verlag, 1995
[10] Suykens J A K, Van Gestel T, De Brabanter J, et al. Least squares support vector machines[M]. World Scientific Publishing, 2002
[11] Guo H B, Guan X Q. Application of least squares support vector regression in network flow forecasting[C]//Proceedings of 2nd International Conference on Computer Engineering and Technology, 2010:V7-342-V7-345
[12] Zhao Y H, Zhong P, Wang K N. Application of least squares support vector regression based on time series in prediction of gas[J]. Journal of Convergence Information Technology, 2011,6(1):243-250
[13] Guo Y M, Zhai Z J, Jiang H M. Weighted prediction of multi-parameter chaotic time series using least squares support vector regression (LS-SVR)[J]. Journal of Northwestern Polytechnical University, 2009,27(1):83-86
[14] Shilton A, Daniel T H L, Palaniswami M. A division algebraic framework for multidimensional support vector regression[J]. IEEE Transactions on system, man, and cybernetics-Part B: Cybernetics, 2010,40(2):517-528
[15] Tang W M. New forecasting model based on grey support vector machine[J]. Journal of Systems Engineering, 2006,21(4):410-413
[16] Zhan D H, Bi Y X, et al. Power load forecasting method based on series grey neural network[J]. System Engineering-Theory & Practice, 2004,23:128-132
[17] Deng J L. The primary methods of grey system theory[M]. Wuhan: Huazhong University of Science and Technology Press, 2004 (in Chinese)
[18] Zhang Y. Enhanced statistical analysis of nonlinear process using KPCA[J]. KICA and SVM. Chem. Eng. Sci., 2009,64:801-811
[20] Ojeda F, Suykens J, De Moor B. Low rank update LS-SVM classifiers for fast variable selection[J]. Neural Network, 2008,21:443-449
[21] Remaki L, Cheriet M. KCS-new kernel family with compact support in scale space: formulation and impact[J]. IEEE Transactions on Image Processing, 2009,9(6):970-981
[22] Huang X. The study on kernels in support vector machine[D]. Suzhou: Suzhou University, 2008 (in Chinese)
[23] Ye M Y, Wang X D, Zhang H R. Chaotic time series forecasting using online least squares support vector machine regression[J]. ACTA Physica Sinica, 2005,54:2568-2573 (in Chinese)