In this study, we propose a new hybrid approach for time series prediction based on the
efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms …

This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model
and predict time series. The multi-step supervised-learning based-on-gradient methods as …

This paper proposes a two-stage prediction model, for multivariate time series prediction
based on the efficient capabilities of evolutionary fuzzy cognitive maps (FCMs) enhanced by …

Short-term water demand forecasting is required to increase the stability of urban freshwater
supply resulting in optimal and timely use of water resources. In this article, we design a …

In this chapter, we describe the design of a multi-regressive forecasting model based on
fuzzy cognitive maps (FCMs). Growing window approach and 1-day ahead forecasting are …

The purposes of this research are to find a model to forecast the electricity consumption in a
household based on fuzzy cognitive map (FCM) prediction capabilities. The data analysis …

This paper is devoted to the multi-step ahead prediction of multivariate time series with the
use of fuzzy cognitive maps. The aim of the analysis is to predict the electricity consumption …

A novel design method for time series modeling and prediction with fuzzy cognitive maps
(FCM) is proposed in this paper. The developed model exploits the least square method to …

In this paper we propose a new approach to learning fuzzy cognitive maps (FCMs) as a
predictive model for time series forecasting. The first contribution of this paper is the dynamic …

Fuzzy cognitive maps (FCMs) is a knowledge representation tool that can be exploited for
predicting multivariate time-series. FCM model represents dependencies among data …