The fuzzy time series has recently received increasing attention because of its capability in dealing with vague and incomplete data. This article presents an improved fuzzy time series model; we show that our method is as complete as the original definition but with higher reliability (Chou and Lee). Experimental results using the University of Alabama’s enrollment data (adapted by Song and Ch...

A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting probl...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

In this paper, the authors develop a model to estimate future performance of construction projects. For the purpose of estimation, fuzzy times series models are used as an effective approach in estimation process. Furthermore, linguistic terms are applied to interpret the fuzzy-based results. The proposed model can assists project managers to develop their knowledge concerning the future aspect...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

Prediction is a critical component in decision-making process for business management. Fuzzy Markov model is a common approach for dealing with the prediction of time series. However, not many studies devoted their attention to the effect of the parameters on model fitting for fuzzy Markov model. In the paper, we examine the prediction ability for fuzzy Markov model, based on the data of Taiwan...

This paper presents a novel approach to modelling time series datasets using fuzzy trend information. A time series is described using natural linguistic terms such as rising more steeply and falling less steeply. These natural shape descriptors enable us to produce a glass box model of the series. The linguistic shape descriptors are represented in our system by a new feature called the trend ...