In this paper, we propose a new residual analysis method using Fourier series transform into fuzzy time series model for improving the forecasting performance. This hybrid model takes advantage of the high predictable power of fuzzy time series model and Fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...

Fuzzy time series model has been employed by many researchers in forecasting activities such as students’ enrolment, temperature fluctuations and stock prices. The existing fuzzy time series models require exact match of the fuzzy logic relationships to calculate the forecasted value. However, in real life applications, the exact match of fuzzy logic relationships is not possible. Thus, an impr...

This study develops an improved fuzzy time series models for forecasting short-term series data. The forecasts were obtained by comparing the proposed improved fuzzy time series, Hwang’s fuzzy time series, and heuristic fuzzy time series. The tourism from Taiwan to the United States was used to build the sample sets which were officially published annual data for the period of 1991–2001. The ro...

Short term load forecasting can be made effective and closer to actual demand by applying the suggested multi pronged approach of genetic, fuzzy and statistical method as discussed in this paper. Taking the advantages of global search abilities of evolutionary computing as well as expert inference based on statistical aspects, load forecasting can be made nearly error free. The results were com...

the adverse consequences of major accident events have led to development of accident analysis techniques to investigate thoroughly the accidents. however, each technique has its own advantages and shortcomings,which make it very difficult to find a single technique being capable of analyzing all types of accidents. therefore, the comparison of accident analysis techniques would help finding ou...

This paper investigates the use of evolving fuzzy algorithms in forecasting. An Evolving Takagi-Sugeno algorithm (eTS), which is based on a recursive version of the Subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii w...

In our daily life, people are often using forecasting techniques to predict weather, stock, economy and even some important Key Performance Indicator (KPI), and so forth. Therefore, forecasting methods have recently received increasing attention. In the last years, many researchers used fuzzy time series methods for forecasting because of their capability of dealing with vague data. The followe...

Since the pioneering work of Zadeh, fuzzy set theory has been applied to a myriad of areas. Song and Chissom introduced the concept of fuzzy time series and applied some methods to the enrollments of the University of Alabama. In recent years, a number of techniques have been proposed for forecasting based on fuzzy set theory methods. These methods have either used enrollment numbers or differe...

Particle Swarm Optimization (PSO) is proposed in our research to generate Fuzzy Controller, a fuzzy logic control (FLC) is proposed to control manufacturing system presented by mmachine line as an m-order state-space. As results indicated, use particle swarm optimization (PSO) method for optimizing a fuzzy logic controller (FLC) for manufacturing system is better than that of fuzzy logic contro...