Starting from the contribution of such thinkers as the famous Giordano Bruno (1583) and the
great mathematician and physicist Henri Poincaré (1889) and the surprising discovery of the …

Multi-step prediction of chaotic time series has been a challenging problem. An encoder–
decoder architecture based on novel Fourier attention was proposed, called FGNet, applied …

Efficient integration of wind energy requires accurate wind power forecasting. This prediction
is critical in optimising grid operation, energy trading, and effectively harnessing renewable …

Next-generation reservoir computing is a machine-learning approach that has been recently
proposed as an effective method for predicting the dynamics of chaotic systems. So far, this …

A smart grid is a modern electricity system enabling a bidirectional flow of communication
that works on the notion of demand response. The stability prediction of the smart grid …

Accurate forecasting of ship motion is of great significance for ensuring maritime operational
safety and working efficiency. A data-driven ship motion forecast method is proposed in this …

The growing demand for consumer-end electrical load is driving the need for smarter
management of power sector utilities. In today's technologically advanced society, efficient …

Network abnormal traffic detection is a hot topic in network security. Based on the theory of
chaotic neural network, this paper constructs a network traffic anomaly detection model to …

Abstract Unsupervised Domain Adaptation (UDA) is an ideal transfer learning method,
which can use labeled source data to improve the classification performance of unlabeled …

Forecasting is important for social development and industrial production in today's complex
and fluctuating economic environment. The nonlinearity and non-stationarity of financial time …