In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Mid-term electricity demand forecasting plays an important role in ensuring the operational
safety of the power system and the economic efficiency of grid companies. Most studies …

Stock market analysis is extremely important for investors because knowing the future trend
and grasping the changing characteristics of stock prices will decrease the risk of investing …

In the context of escalating global environmental concerns, the importance of preserving
water resources and upholding ecological equilibrium has become increasingly apparent …

Accurate fault detection for unmanned aerial vehicle (UAV) actuators is essential for
ensuring flight safety and mission completion. Without the requirement of modeling complex …

Human activities and climate change have exacerbated the frequency of extreme weather
events such as rainstorms and floods, which makes it difficult to accurately quantify the …

Federated learning (FL) has become a common approach for distributed training of quality of
service (QoS) prediction tasks in smart city solutions. However, FL is vulnerable to …

In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit
from assessing alternative forecasting models of stock index movement. The present …

Intelligent forecasters are now being considered in the stock market, providing essential
insights and strategic guidance to investors and traders by presenting analytical tools and …