Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology

TL;DR

This paper introduces a new fuzzy time series forecasting method using means-based partitioning of historical accident data, significantly improving accuracy over existing approaches.

Contribution

It proposes a novel means-based partitioning approach for fuzzy time series forecasting of car accidents, enhancing accuracy and validation of the methodology.

Findings

The new method outperforms existing fuzzy forecasting methods in accuracy.

It is a kth order, time-variant forecasting approach.

The method is validated on car accident data.

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.