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Support Vector Regression for Warranty Claim Forecasting

Wu, Shaomin, Akbarov, Artur (2011) Support Vector Regression for Warranty Claim Forecasting. European Journal of Operational Research, 213 (1). pp. 196-204. ISSN 0377-2217. (doi:10.1016/j.ejor.2011.03.009) (KAR id:31997)

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Official URL
http://dx.doi.org/10.1016/j.ejor.2011.03.009

Abstract

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. © 2011 Elsevier B.V. All rights reserved.

Item Type: Article
DOI/Identification number: 10.1016/j.ejor.2011.03.009
Additional information: Unmapped bibliographic data: PY - 2011/// [EPrints field already has value set] AD - Cranfield University, School of Applied Sciences, Cranfield, Bedfordshire MK43 0AL, United Kingdom [Field not mapped to EPrints] JA - Eur J Oper Res [Field not mapped to EPrints]
Uncontrolled keywords: Multilayer perceptron, Neural networks, Radial basis function network, Support vector regression, Warranty claims, Artificial neural network models, Information loss, Model evaluation, Multi layer perceptron, Multi-layer perceptrons, Poisson model, Repair rate, Support vector regression, Support vector regression models, Support vector regressions, Time series models, Warranty claims, Electric loads, Forecasting, Learning algorithms, Multilayer neural networks, Multilayers, Pattern recognition systems, Regression analysis, Time series, Vectors, Radial basis function networks
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Shaomin Wu
Date Deposited: 26 Oct 2012 15:30 UTC
Last Modified: 16 Feb 2021 12:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31997 (The current URI for this page, for reference purposes)
Wu, Shaomin: https://orcid.org/0000-0001-9786-3213
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