IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v92y2007i4p423-432.html
   My bibliography  Save this article

Forecasting systems reliability based on support vector regression with genetic algorithms

Author

Listed:
  • Chen, Kuan-Yu

Abstract

This study applies a novel neural-network technique, support vector regression (SVR), to forecast reliability in engine systems. The aim of this study is to examine the feasibility of SVR in systems reliability prediction by comparing it with the existing neural-network approaches and the autoregressive integrated moving average (ARIMA) model. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using real-value genetic algorithms, and then adopts the optimal parameters to construct the SVR models. A real reliability data for 40 suits of turbochargers were employed as the data set. The experimental results demonstrate that SVR outperforms the existing neural-network approaches and the traditional ARIMA models based on the normalized root mean square error and mean absolute percentage error.

Suggested Citation

  • Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:4:p:423-432
    DOI: 10.1016/j.ress.2005.12.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832006000147
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2005.12.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcos Álvarez-Díaz & Alberto Álvarez, 2002. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0205, Universidade de Vigo, Departamento de Economía Aplicada.
    2. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    3. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    4. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    5. Mustapha Djennas & Mohamed Benbouziane & Meriem Djennas, 2011. "An Approach of Combining Empirical Mode Decomposition and Neural Network Learning for Currency Crisis Forecasting," Working Papers 627, Economic Research Forum, revised 09 Jan 2011.
    6. Yanqin Bai & Xin Yan, 2016. "Conic Relaxations for Semi-supervised Support Vector Machines," Journal of Optimization Theory and Applications, Springer, vol. 169(1), pages 299-313, April.
    7. Helder Sebastião & Pedro Godinho & Sjur Westgaard, 2020. "Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(si), pages 1-17, December.
    8. Hong-Yu Lin & Kuentai Chen, 2015. "The Trend of Average Unit Price in Taipei City," Research in World Economy, Research in World Economy, Sciedu Press, vol. 6(1), pages 133-142, March.
    9. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
    10. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    11. Marius Lux & Wolfgang Karl Härdle & Stefan Lessmann, 2020. "Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid," Computational Statistics, Springer, vol. 35(3), pages 947-981, September.
    12. Shaogao Lv & Yongchao Hou & Hongwei Zhou, 2019. "Financial Market Directional Forecasting With Stacked Denoising Autoencoder," Papers 1912.00712, arXiv.org.
    13. Georgi Nalbantov & Philip Hans Franses & Patrick Groenen & Jan Bioch, 2010. "Estimating the Market Share Attraction Model using Support Vector Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 688-716.
    14. T. Law & J. Shawe-Taylor, 2017. "Practical Bayesian support vector regression for financial time series prediction and market condition change detection," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1403-1416, September.
    15. Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
    16. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    17. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    18. Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
    19. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    20. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:92:y:2007:i:4:p:423-432. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.