This paper links testing of non-stationary time series features to the selection of fuzzy model for time series prediction. The data for model test are obtained from AREMOS, Taiwan. Empirical results show that fuzzy time series models have different performance patterns in predicting non-stationary time series. Data with a clear time trend, such as consumption, exports or other macroeconomic da...

Qualitative evaluation and comparison of changes of indications of objects having different nature is used by designers, managers, people making decisions (PMD) and experts to make the decisions more reasonable. For suport of such activity on the analysis of changes of data connected with certain dates and time intervals, models of fuzzy time series are applied. In this article a model of fuzzy...

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relat...

There is a significant problem associated with the fuzzy time series. That is a strict increasing and decreasing case. Under the discussion case, fuzzy time series model arise a continuous increasing/decreasing forecasting value. From the illustrative example, we can see that our definition not only define the trend of the fuzzy numbers that represent the linguistic values of the linguistic var...

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic t...

Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternati...

Foreign exchange rate is a chaotic time series which is consistent with the MackeyGlass equation. Fuzzy logic is an intelligent computational technique and has good potential in forecasting time-series data. This study uses fuzzy logic to study data of exchange rates and build a dynamic adaptive neuron-fuzzy logic forecasting model. The performance of the model built is compared with an autoreg...

The fuzzy time series approaches, which recently are intensively considered by the researchers, consist of three stages of fuzzification, determination of fuzzy relations and defuzzification. Several studies using different approaches in these steps have been conducted in literature. In most of the studies related fuzzy time series, the membership degrees of belonging to every fuzzy set of each...

In this paper, we provide theoretical justification for the application of higher degree fuzzy transform in time series analysis. Under the assumption that a time series can be additively decomposed into a trend-cycle, a seasonal component and a random noise, we demonstrate that the higher degree fuzzy transform technique can be used for the estimation of the trend-cycle, which is one of the ba...