Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting problems in which historical data are linguistic values. In this paper, we present a new fuzzy time series forecasting model, which uses the historical data as the universe of discourse...

Time series forecasting in highly nonlinear and chaotic systems is a challenging research area with a variety of applications in economics, environmental sciences and various fields of engineering. This paper presents a novel Locally Quadratic Fuzzy Neural Model (LQFNM) to forecast the behavior of highly nonlinear and chaotic time series. It is based on the idea of approximating a nonlinear fun...

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...

The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets fuzzy numbers and a fuzzy linear regression model is described as a fuzzy function with fuzzy numbers as vague regression parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used....

Prediction has been widely studied for many years as time series analysis (Box & Jenkins, 1970; Tong, 1990). Traditionally, prediction is based on a statistical model that is either linear or nonlinear (Li et al., 1990). Recently, several studies have adopted neural fuzzy networks to predict time series (Cowder, 1990; Kasabov & Song, 2002; Ling et al., 2003). Researchers have discussed that the...

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...