0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.04.001 * Corresponding author. E-mail address: [email protected] (C.H. Aladag 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. Definin...

Principally a new approach to detection of anomalies in geophysical records is connected with a fuzzy mathematics application. The theory of discrete mathematical analysis and collection of algorithms for time series processing constructed on its basis represent results of this research direction. These algorithms are the consequence of fuzzy modeling of logic of an interpreter who visually rec...

This paper continues the development of the innovative method for time series analysis and forecasting using special soft-computing techniques: fuzzy (F-) transform and Fuzzy Natural Logic. We will demonstrate that the F-transform is a proper technique for extraction of the trend-cycle of time series. Furthermore, we will elaborate in more detail automatic generation of linguistic evaluation of...

The paper presents IF-inference systems of Takagi-Sugeno type. It is based on intuitionistic fuzzy sets (IF-sets), introduced by K.T. Atanassov, fuzzy t-norm and t-conorm, intuitionistic fuzzy t-norm and t-conorm. Thus, an IFinference system is developed for ozone time series prediction. Finally, we compare the results of the IF-inference systems across various operators.

A time series is a realization or sample function from a certain stochastic process. The main goals of the analysis of time series are forecasting, modeling and characterizing. Conventional time series models i.e. autoregressive (AR), moving average (MA), hybrid AR and MA (ARMA) models, assume that the time series is stationary. The other methods to model time series are soft computing techniqu...

Many researchers have used fuzzy logic system to predict the time series data. In fuzzy system, the crisp data are converting into fuzzy data based on membership function. The futuristic data is predicted using previous data and fuzzy relation. But, in fuzzy system, there are many existing and derived membership functions which are used to fuzzify data. In this paper, an effort has been made to...

Article history: Received 2 December 2013 Received in revised form 27 August 2014 Accepted 7 September 2014 Available online 18 September 2014

In this paper, we discuss three tasks of mining information from time series, namely finding intervals with monotonous trend, evaluation of it in sentences of natural language, and summarizing information using intermediate quantifiers. The mined information is presented in simple sentences of natural language. For estimation of the trend of time series, we apply the theory of first-degree F-tr...

We present a destructive (pruning) method aiming at gradually nding the appropriate number of rules in the case of fuzzy models. A particular attention has been paid to Takagi–Sugeno fuzzy systems (TS) for the problem of functions approximation. The proposed system can be seen as a generalization of the conventional TS system and allows to evaluate the importance of one particular rule in the i...