Fuzzy set theory has been used to model systems that are hard to define precisely. As a
methodology, fuzzy set theory incorporates imprecision and subjectivity into the model …

Recently, there seems to be increased interest in time series forecasting using soft
computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set …

In this paper, we propose a new fuzzy time series forecasting method for forecasting the
Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time …

A drawback of existing fuzzy forecasting methods based on fuzzy time series is that they use
the first-order fuzzy time series to deal with forecasting problems in which the forecasting …

Given the high potential benefits and impacts of accurate stock market predictions,
considerable research attention has been devoted to time series forecasting for stock …

This study proposes weighted models to tackle two issues in fuzzy time series forecasting,
namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were …

In [6–9], Song et al. proposed fuzzy time-series models to deal with forecasting problems. In
[10], Sullivan and Woodall reviewed the first-order time-invariant fuzzy time series model …

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 …

Time series analysis and forecasting future values has been a major research focus since
years ago. Time series analysis and forecasting in time series data finds it significance in …

In recent years, many methods have been proposed for forecasting enrollments based on
fuzzy time series. However, the forecasting accuracy rates of the existing methods are not …