A Fuzzy Time Series Model in Road Accidents Forecast

Abstract

Many researchers have explored fuzzy time series forecasting models with the purpose to improve accuracy. Recently, Liu et al., have proposed a new method, which an improved version of Hwang et al., method. The method has proposed several properties to improve the accuracy of forecast such as levels of window base, length of interval, degrees of membership values, and existence of outliers. Despite these improvements, far too little attention has been paid to real data applications. Based on these advantageous, this paper investigates the feasibility and performance of Liu et al., model to Malaysian road accidents data. Twenty eight years of road accidents data is employed as experimental datasets. The computational results of the model show that the performance measure of mean absolute forecasting error is less than 10 percent. Thus it would be suggested that the Liu et al., model practically fit with the Malaysian road accidents data.