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 rapid advancements in high-throughput techniques have fueled large-scale production
of biological data at very affordable costs. Some of these techniques are microarrays and …

Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs).
However, it is required to develop techniques capable of effective responses and great …

Accurate wind power forecasting can effectively reduce the adverse effects of wind power
forecasting errors on wind power grid integration and power dispatch. However, current …

Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary
time series. However, it still remains challenging to deal with large-scale nonstationary time …

Fuzzy cognitive maps have achieved significant success in time series modeling and
forecasting. However, fuzzy cognitive maps still contain weakness to handle the …

Fuzzy cognitive maps (FCMs) have emerged as a powerful tool for dealing with the task of
time series prediction. Most existing research devoted to designing an effective method to …

Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the
excellent performance in dynamic system modeling. However, existing time series prediction …

The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved.
Although many methods have been proposed to cope with this issue, the performance of …

Time series data are usually non-stationary and evolve over time. Even if deep learning has
been found effective in dealing with sequential data, the stability of deep neural networks in …