In recent years, artificial neural networks (ANN) have been widely used in real life time series forecasting. Artificial neural networks can model both linear and curvilinear structure in time series. Most of the conventional ...

Fuzzy time series, subjected to many scientific studies, have been used in forecasting in recent years. Due to their uncertainty, time series encountered in daily life should be perceived as fuzzy time series and analyzed ...

Due to the vagueness that they contain in their observations, fuzzy time series models worked in two main categories such as first order and high order models, has an ever expending field of study. Fuzzy time series analysis ...

Forecast combination is a method used for obtaining more accurate forecasts. Forecast combination consists of the combination of forecasts obtained from different models with various methods. There are several types of ...

In the literature, different selection criteria are used for determining the best architecture when time series is analyzed by artificial neural networks. Criteria available in the literature measure different properties ...

In recent years, studies including long memory time series are existed in the literature. Such time series in real life may have both linear and nonlinear structures. Linear models are inadequate for this kind of time ...

The researchers from various fields have been studying on time series forecasting for approximately one century in order to get better forecasts for the future.To achieve high forecast accuracy level, various time series ...

Most of the time series faced in real life are fuzzy time series and these time series have to be forecasted by fuzzy time series forecasting methods. Therefore, there have been many studies in the literature in which ...

The idea of exploring fuzzy set theory to time series forecasting issues has been enormously attracted researcher's attention in recent years. Several new approaches on fuzzy time series have been put forward. These ...

Fuzzy time series approaches have been recently used for forecasting in many studies [1]. These approaches can be categorized into two subclasses that are univariate and multivariate approaches. It is a fact that many ...