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Support vector regression for warranty claim forecasting


  • Wu, Shaomin
  • Akbarov, Artur


Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation. To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.

Suggested Citation

  • Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
  • Handle: RePEc:eee:ejores:v:213:y:2011:i:1:p:196-204

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    References listed on IDEAS

    1. Murthy, D. N. P. & Djamaludin, I., 2002. "New product warranty: A literature review," International Journal of Production Economics, Elsevier, vol. 79(3), pages 231-260, October.
    2. Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
    3. Wu, Shaomin & Li, Huiqing, 2007. "Warranty cost analysis for products with a dormant state," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1285-1293, November.
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    5. Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.
    6. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
    7. David Stephens & Martin Crowder, 2004. "Bayesian analysis of discrete time warranty data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 195-217.
    8. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    9. repec:eee:reensy:v:92:y:2007:i:2:p:243-251 is not listed on IDEAS
    10. Kim, Bowon & Park, Sangsun, 2008. "Optimal pricing, EOL (end of life) warranty, and spare parts manufacturing strategy amid product transition," European Journal of Operational Research, Elsevier, vol. 188(3), pages 723-745, August.
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    1. repec:eee:reensy:v:145:y:2016:i:c:p:147-154 is not listed on IDEAS
    2. Gür Ali, Özden & Yaman, Kübra, 2013. "Selecting rows and columns for training support vector regression models with large retail datasets," European Journal of Operational Research, Elsevier, vol. 226(3), pages 471-480.
    3. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    4. repec:eee:ejores:v:263:y:2017:i:2:p:540-558 is not listed on IDEAS
    5. Wu, Shaomin, 2014. "Construction of asymmetric copulas and its application in two-dimensional reliability modelling," European Journal of Operational Research, Elsevier, vol. 238(2), pages 476-485.
    6. repec:eee:proeco:v:193:y:2017:i:c:p:316-331 is not listed on IDEAS
    7. repec:eee:energy:v:154:y:2018:i:c:p:143-156 is not listed on IDEAS
    8. He, Yaoyao & Liu, Rui & Li, Haiyan & Wang, Shuo & Lu, Xiaofen, 2017. "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory," Applied Energy, Elsevier, vol. 185(P1), pages 254-266.
    9. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    10. repec:eee:reensy:v:106:y:2012:i:c:p:160-164 is not listed on IDEAS
    11. repec:eee:reensy:v:116:y:2013:i:c:p:126-134 is not listed on IDEAS


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