Syed Muhammad Aqil Burney

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.

M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data

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.

New Method of Learning and Knowledge Management in Type-I Fuzzy Neural Networks

A new method for modeling and knowledge extraction at each neuron of a neural network using type-I fuzzy sets is presented. This approach of neuron modeling provides a new technique to adjust the fuzzy neural network (FNN) structure for feasible number of hidden neurons and efficient reduction in computation complexity. Through repeated simulations of a crisp neural network, we propose the idea that for each neuron in the network, we can obtain reduced model with high efficiency using wavelet based multiresolution analysis (MRA) to form wavelet based fuzzy weight sets (WBFWS). Triangular and Gaussian membership functions (MFs) are imposed on wavelet based crisp weight sets to form Wavelet Based Quasi Fuzzy Weight Sets (WBQFWS) and Wavelet Based Gaussian Fuzzy Weight Sets (WBGFWS). Such type of WBFWS provides good initial solution for training in type-I FNNs. Thus the possibility space for each synoptic connection is reduced significantly, resulting in fast and confident learning of FNNs. It is shown that propsed modeling approach hold low computational complexity as compared to existing type-I fuzzy neural network models.