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Hybrid SVM and ARIMA Model for Failure Time Series Prediction based on EEMD

Volume 15, Number 4, April 2019, pp. 1161-1170
DOI: 10.23940/ijpe.19.04.p11.1611170

Haiyan Suna, Jing Wua, Ji Wub, and Haiyan Yangb

aSchool of Mathematics and Systems Science, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China
bSchool of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China


(Submitted on October 23, 2018; Revised on November 24, 2018; Accepted on December 26, 2018)

Abstract:

A more widely used hybrid model of support vector regression (SVR) and autoregressive integrated moving average (ARIMA) based on Ensemble Empirical Mode Decomposition (EEMD) is proposed for failure time series prediction by taking advantage of the SVR model to forecast the nonlinear part of failure time series and the ARIMA model to predict the linear basic part. It firstly uses EEMD to decompose the original failure sequence into several significant fluctuation components and a trend component, and then it utilizes SVR and ARIMA to forecast them separately. The performance of the presented model is measured against other unitary models such as Holt-Winters, autoregressive integrated moving average, multiple linear regression, and group method of data handling of seven published nonlinear non-stationary failure datasets. The comparison results indicate that the proposed model outperforms other techniques and can be utilized as a promising tool for failure data forecast applications.

References: 21

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    1.        M. Xie, K. L. Poh, and Y. S. Dai, “Computing System Reliability: Models and Analysis,” Springer Publishing Company, Incorporated, 2014

    2.        B. Yang, X. Li, M. Xie, and F. Tan, “A Generic Data-Driven Software Reliability Model with Model Mining Technique,Reliability Engineering and System Safety, Vol. 95, No. 6, pp. 671-678, 2010

    3.        M. D. C. Moura, E. Zio, I. D. Lins, and E. Droguett, “Failure and Reliability Prediction by Support Vector Machines Regression of Time Series Data,Reliability Engineering and System Safety, Vol. 96, No. 11, pp. 1527-1534, 2011

    4.        H. Li, M. Zeng, M. Lu, X. Hu, and Z. Li, “Adaboosting-based Dynamic Weighted Combination of Software Reliability Growth Models,Quality and Reliability Engineering International, Vol. 28, No.1, pp. 67-84, 2012

    5.        R. Li and R. Kang. Research on Failure Rate Forecasting Method based on ARMA Model,Systems Engineering and Electronics, Vol. 30, No. 8, pp. 1588-1591, 2008

    6.        K. Xu, M. Xie, L. C. Tang, and S. L. Ho, Application of Neural Networks in Forecasting Engine Systems Reliability,Applied Soft Computing, Vol. 2, No. 4, pp. 255-268, 2003

    7.        J. L. Harvill and B. K. Ray, “A Note on Multi-Step Forecasting with Functional Coefficient Autoregressive Models,International Journal of Forecasting, Vol. 21, No. 4, pp. 717-727, 2005

    8.        W. C. Wang, C. Kwokwing, D. M. Xu, and X. Y. Chen, “Improving Forecasting Accuracy of Annual Runoff Time Series using Arima based on Eemd decomposition,Water Resources Management, Vol. 29, No. 8, pp. 2655-2675, 2015

    9.        V. Plakandaras, R. Gupta, and P. Gogas, Forecasting the U. S. Real House Price Index, Economic Modelling, pp. 259-267, 2015

    10.     M. Žvokelj, S. Zupan, and I. Prebil,EEMD-Based Multiscale ICA Method for Slewing Bearing Fault Detection and Diagnosis,Journal of Sound and Vibration, pp. 394-423, 2016

    11.     W. U. Zhaohua and N. E. Huang, “Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method,Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp. 1-41, 2011

    12.     V. Vapnik, “The Nature of Statistical Learning Theory,” in Proceedings of Conference on Artificial Intelligence, Springer-Verlag, pp. 988-999, 1995

    13.     G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, “Time Series Analysis: Forecasting and Control (Revised Edition),Journal of Marketing Research, Vol. 14, No. 2, pp. 199-201, 1994

    14.     J. G. Lou and J. H. Jiang. “Correlation Analysis of Software Failure Time Data: Correlation Analysis of Software Failure Time Data,Journal of Computer Applications, Vol. 30, No. 3, pp. 600-602, 2010

    15.     X. L. Liu, D. X. Jia, L. I. Hui, and J. Y. Jiang, “Research on Kernel Parameter Optimization of Support Vector Machine in Speaker Recognition,Science Technology and Engineering, 2010

    16.     C. L. Wu, K. W. Chau, and Y. S. Li, “River Stage Prediction based on a Distributed Support Vector Regression,Journal of Hydrology, Vol. 358, No. 1, pp. 96-111, 2008

    17.     D. Bratton and J. Kennedy, “Defining a Standard for Particle Swarm Optimization, in Proceedings of IEEE Swarm Intelligence Symposium, pp. 120-12, IEEE Computer Society, 2007

    18.     P. F. Pai and W. C. Hong, “Software Reliability Forecasting by Support Vector Machines with Simulated Annealing Algorithms,Journal of Systems and Software, Vol. 79, No. 6, pp. 747-755, 2006

    19.     V. Cherkassky and Y. Ma, “Practical Selection of SVM Parameters and Noise Estimation for SVM Regression,Neural Networks the Official Journal of the International Neural Network Society, Vol. 17, No. 1, pp. 113-126, 2004

    20.     J. D. Musa,Software Reliability Data,Technical Report in Rome Air Development Center, 1979

    21.     H. Sun, L. Zhang, J. Wu, J. Wu, and H. Yang, “A New Method of Model Combination based on the NHPP Software Reliability Models,” in Proceedings of International Conference, pp. 153-158, 2018

     
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