Abstract—Study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improve forecasting accuracy. Recently, Fuzzy time-series based on Fibonacci sequence has been proposed as a new fuzzy time series model which incorporates the concept of the Fibonacci sequence, the f...

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuz...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.040 ⇑ Corresponding author. Tel.: +90 312 2977900. E-mail address: [email protected] (C.H. Alad In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural net...

Fuzzy time series forecasting methods has got more and more attention in recent years since they have a good capability of forecasting real-world time series which contains uncertainty. There have been various fuzzy time series forecasting methods in the literature. On the other hand, just a few ones have been proposed to forecast seasonal time series. When a seasonal time series is forecasted,...

Fuzzy rule based systems are increasingly being used to deal with time series processes that may lack stochastic stability due to non-stationarity, multiscaling and persistent autocorrelations. Wavelet filtering can be used to deal with such phenomenon. A method for creating a fuzzy-rule base from a time series, where the first difference (returns) of the preprocessed series is used, and high f...

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...

This paper presents a novel approach to modelling time series datasets using fuzzy trend information. A time series is described using natural linguistic terms such as rising more steeply and falling less steeply. These natural shape descriptors enable us to produce a glass box model of the series. The linguistic shape descriptors are represented in our system by a new feature called the trend ...