# Inaccuracy Minimization By Partitioning Fuzzy Data Sets - Validation Of An Analytical Methodology

## Arutchelvan.G

 Department of Computer Science and Applications Adhiparasakthi College of Arts and Science G.B.Nagar, Kalavai , India garutchelvan@yahoo.com Abstract— In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However , the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on means based partitioning of the historical data of car road accidents. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting the car road accidents than the existing methods. Keywords- Fuzzy sets, Fuzzy logical groups, fuzzified data, fuzzy time series. 

## I. Introduction

Forecasting activities play an important role in our daily life. 

During last two decades, various approaches have been developed for time series forecasting. Among them, ARMA models and Box-Jenkins model building approaches are highly famous. However, the classical time series methods can not deal with forecasting problems in which the values of time series are linguistic terms represented by fuzzy sets [11], [23]. Therefore, Song and Chissom [18] presented the theory of fuzzy time series to overcome this drawback of the classical time series methods. Based on the theory of fuzzy time series, Song et al. presented some forecasting methods [16], [18], 
[19], [20] to forecast the enrollments of the University of Alabama. In [1] Chen and Hsu and in [2], Chen presented a method to forecast the enrollments of the University of Alabama based on fuzzy times series. It has the advantage of reducing the calculation, time and simplifying the calculation process. In [8], Hwang, Chen and Lee used the differences of the enrollments to present a method to forecast the enrollments of the University of Alabama based on fuzzy time series. In [5] and [6], Huang used simplified calculations with the addition of heuristic rules to forecast the enrollments using [2]. 

Dr.Sivatsa S.K **Dr. Jaganathan. R** 
Professor (Retired), Deputy Registrar(Edn) 
Dept. of Electronics Engineering, Vinayaka Mission Madras Institute of Technology, University, Chennai, Anna University,Chennai, India India, profsks@rediffmail.com Jaganr1@yahoo.com In [4], Chen presented a forecasting methods based on highorder fuzzy time series for forecasting the enrollments of the University of Alabama. In [3], Chen and Hwang presented method based on fuzzy time series to forecast the daily temperature. In [15], Melike and Konstsntin presented a new first order time series model for forecasting enrollments of the University of Alabama. In [14], Li and Kozma presented a dynamic neural network method for time series prediction using the KIII model. In [21], Su and Li presented a method for fusing global and local information in predicting time series based on neural networks. In [22], Sullivan and Woodall reviewed the first-order time-variant fuzzy time series model and the first-order time-invariant fuzzy time series model presented by Song and Chissom[18], where their models are compared with each other and with a time-variant Markov model using linguistic labels with probability distributions. In [13], Lee, Wang and Chen presented two factor high order fuzzy time series for forecasting daily temperature in Taipei and TAIFEX. In [9], Jilani and Burney and in [10], Jilani, Burney and Ardil presented new fuzzy metrics for high order multivariate fuzzy time series forecasting for car road accident casualties in Belgium. 

In this paper, we present a comparison of our proposed method and existing fuzzy time series forecasting methods to forecast the car road accidents in Belgium. Our proposed method belongs to the class of k-step first-order univariate time-variant method. The proposed method gives best forecasting accuracy rate for forecasting the car road accidents when compared with existing methods. The rest of this paper is organized as follows. In section2, we briefly review some basic concepts of fuzzy time series. In Section 3, we present our method of fuzzy forecasting based on means of partitioning the car road accidents data. In Section 4, we compared the forecasting results of the proposed method with the existing methods. In section 5, we conclude the results. 