Multivariate stochastic fuzzy forecasting models

In this paper, we have presented two new multivariate fuzzy time series forecasting methods. These methods assume m-factors with one main factor of interest. Stochastic fuzzy dependence of order k is assumed to define general methods of multivariate fuzzy time series forecasting and control. These new methods are applied for forecasting total number of car road accidents casualties 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. National Institute of Statistics, Belgium provides risk intensity based classification of each city. Thus, this work provides support in deciding the appropriate risk associated with an insured in a particular area.