Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

Nazirah Ramli,Daud Mohamad,Siti Musleha Ab Mutalib

doi:10.14419/ijet.v7i4.30.22284

Abstract

This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

Highlights

To overcome the drawback in the classical time series method, [1] proposed the fuzzy time series (FTS) prediction model
A large number of studies have been carried out to improve the procedure of FTS in [1] such as by [2,3,4]. [2] proposed a model to improve the length of the interval by utilizing a new method and [3] proposed the FTS forecasting model which can deal with seasonal time series data
In another study, [4] proposed a higher orderforecasting model based on automatic grouping strategy and generalized fuzzy logical relationship (FLR). [5,6] used trapezoidal fuzzy numbers (TrFNs) to denote the linguistic term of the data, and produced the forecast values of TrFNs form. [1,2,3,4,5,6] defuzzified the forecast values to crisp values, and the forecasting accuracy such as mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) was calculated

Summary

Introduction

To overcome the drawback in the classical time series method, [1] proposed the fuzzy time series (FTS) prediction model. The discrete fuzzy set was used to represent the time series data, and the forecast value in terms of discrete fuzzy set was produced. [2] proposed a model to improve the length of the interval by utilizing a new method and [3] proposed the FTS forecasting model which can deal with seasonal time series data. [5,6] used trapezoidal fuzzy numbers (TrFNs) to denote the linguistic term of the data, and produced the forecast values of TrFNs form. This paper proposes an improved fuzzy forecasting model based on frequency density [7], and area and height similarity measure [8]. The forecasting accuracy of this FTS model is based totally on the degree of similarity between the forecast values and historical values

Preliminaries

Proposed Fuzzy Time Series Forecasting

Findings

Conclusion