The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy ...

This paper presents pattern recognition of time series data and subsequent temporal data mining of power signal disturbance events that occur frequently in power distribution networks using multiresolution S-transform and Fuzzy neural inference system . This system yields relevant features, which are used in a Fuzzy expert system to separate the transient time series data and steady state short...

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...

In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods ...

The application of fuzzy time series models to forecasting has been drawing a great amount of attention. To provide a more sophisticated model to handle real world problems thus becomes important. This study intends to model fuzzy time series with multiple observations at a single time point. The proposed model shows how to fuzzify multiple observations into a fuzzy set. Neural networks are app...

In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However , the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our m...

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...

High-dimensional time series increasingly arise in the Internet of Energy (IoE), given use multi-sensor environments and two way communication between energy consumers smart grid. Therefore, methods that are capable computing high-dimensional great value building IoE applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric easy implementation high accuracy. Unfortuna...

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...

In this paper we describe a method to nd the Instantaneous Secretion Rate of a hormone. The dynamics of the relation between secretion rate and peripheral plasma hormone concentration is rst identiied by using learning signals. The fuzzy identii-cation method is based on fuzzy clustering and optimal output predefuzziication. The proposed method leads to a fuzzy inference system which is able to...