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 …

For time series prediction tasks, the autoregressive integrated moving average (ARIMA)
model is one of the most classical and popular linear models, and extended applications …

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 …

Accurate prediction of the stock market trend can assist efficient portfolio and risk
management. In recent years, with the rapid development of deep learning, it can make the …

Achieving short-term accurate photovoltaic power forecasting is of great significance to
improve the efficiency of grid operation, especially in power stations where historical values …

Although time series prediction is widely used to estimate the future state of complex
systems in various industries, accurate, interpretable and generalizable methods are still …

Accurate traffic speed forecasting is critical in advanced transportation management and
traveler route planing. Considering the important influences of spatial–temporal factors and …

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 …

This paper proposes a novel two-stage method for imputing missing SCADA data of wind
turbines with high accuracy based on deep nonparametric models, sparse autoencoders …