Abstract
In this paper, we have presented new multivariate fuzzy time series (FTS) forecasting method. This method assume m-factors with one main factor of interest. Stochastic fuzzy dependence of order k is assumed to define general method of multivariate FTS forecasting and control. This new method is applied for forecasting total number of car road accidents causalities in Belgium using four secondary factors. Practically, in most of the situations, actuaries are interested in analysis of the patterns of casualties in road accidents. Such type of analysis supports in deciding approximate risk classification and forecasting for each area of a city. This directly affects the underwriting process and adjustment of insurance premium, based on risk intensity for each area. Thus, this work provides support in deciding the appropriate risk associated with an insured in a particular area. Finally, comparison is also made with most recent available work on fuzzy time series forecasting.
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Jilani, T.A., Burney, S.M.A. (2007). M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data. In: Melin, P., Castillo, O., RamÃrez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_25
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DOI: https://doi.org/10.1007/978-3-540-72432-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72431-5
Online ISBN: 978-3-540-72432-2
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