Fuzzy Time Series Approaches Research Articles

Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations

A given observation in time series does not only depend on preceding one but also previous ones in general. Therefore, high order fuzzy time series approach might obtain better forecasts than does first order fuzzy time series approach. Defining fuzzy relation in high order fuzzy time series approach are more complicated than that in first order fuzzy time series approach. A new proposed approach, which uses feed forward neural networks to define fuzzy relation in high order fuzzy time series, is introduced in this paper. The new proposed approach is applied to well-known enrollment data for the University of Alabama and obtained results are compared with other methods proposed in the literature. It is found that the proposed method produces better forecasts than the other methods.

Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost

A main drawback of existing fuzzy time series forecasting methods is that they lack persuasiveness in determining universe of discourse and the length of intervals. Two approaches are proposed for overcoming the problem, and the proposed approaches are more objective and reasonable to improve the persuasiveness in determining the universe of discourse, length of intervals and membership functions of fuzzy time series. The first approach is using Minimize Entropy Principle Approach (MEPA) to partition the universe of discourse and build membership functions, and the second is using Trapezoid Fuzzification Approach (TFA). Monthly amount data of IT project expenditure of a company are used to evaluate the performance of the proposed approaches. The forecasting accuracies of the proposed approaches are better than those of previous methods.

Fuzzy time series approach for disruption prediction in tokamak reactors

Disruption in a Tokamak reactor is a sudden loss of confinement that can cause damage to the machine walls and support structures. In this paper, we propose the use of a fuzzy time series (FTS) approach for detection of disruption in Tokamaks. In particular, two-factors FTS models have been exploited for the prediction of the time to disruption in the ASDEX-Upgrade machine. The concept of fuzzy logic is used taking into account that previous techniques make use of expert knowledge for deciding about the onset of a disruption.