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 …

Research on forecasting methods of time series data has become one of the hot spots. More
and more time series data are produced in various fields. It provides data for the research of …

We propose a combined method that is based on the fuzzy time series (FTS) and
convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in …

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 …

Wind speed forecasting is fundamental to the dispatching, controllability, and stability of the
power grid. As a challenging but essential work, wind speed forecasting has attracted …

This study presents an integrated system where wavelet transforms and recurrent neural
network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are …

Length of intervals affects forecasting results in fuzzy time series. Unfortunately, the issue of
how to determine effective lengths of intervals has not been touched in previous studies …

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 …