Among various soft computing approaches for time series forecasting, fuzzy cognitive maps
(FCMs) have shown remarkable results as a tool to model and analyze the dynamics of …

The purpose of this paper is to present the results of a systematic literature review regarding
the development of fuzzy-based models for time series forecasting in the period 2017–2021 …

Solar photovoltaic energy has experienced significant growth in the last decade, as well as
the challenges related to the intermittency of power generation inherent to this process. In …

Time series arise in many fields of science such as engineering, economy and agriculture to
cite a few. In the early 1990's the so called Fuzzy Time Series were proposed to handle …

Abstract Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and
forecasting time series due to their advantages in dealing with uncertainty and simulating …

Abstract Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted
digraph method consisting of nodes (concepts) and weights which represent the …

Time series forecasting is a powerful tool in planning and decision making, from traditional
statistical models to soft computing and artificial intelligence approaches several methods …

Various studies indicate that Fuzzy Time Series (FTS) methods can obtain high accuracy in
a variety of forecasting applciations. However, weighted FTS methods tend to show …

In the present work we extend the traditional C4. 5 decision tree method for regression and
forecasting of multivariate time series. In the proposed method, time series data is first …

Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph
method consisting of nodes (concepts) and weights which represent the dependencies …