As a core branch of financial forecasting, stock forecasting plays a crucial role for financial
analysts, investors, and policymakers in managing risks and optimizing investment …

Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure
and function of the human brain. Their popularity has increased in recent years due to their …

Financial time series prediction has attracted considerable interest from scholars, and
several approaches have been developed. Among them, decomposition-based methods …

Abstract Multivariate Time Series (MTS) forecasting is a fundamental task with numerous
real-world applications, such as transportation, climate, and epidemiology. While a myriad of …

The ability to predict stock prices is essential for informing investment decisions in the stock
market. However, the complexity of various factors influencing stock prices has been widely …

Stock price prediction has long been a critical area of research in financial modeling. The
inherent complexity of financial markets, characterized by both short-term fluctuations and …

For the purpose of managing financial risk and making investment decisions, interval stock
price forecasting is essential. Currently, decomposition integration frameworks are widely …

The financial market, as a complex nonlinear dynamic system frequently influenced by
various factors, such as international investment capital, is very challenging to build trading …

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified
through numerical models based on hydrologic parameters and physics-based equations …

Due to the complexity and volatility of stock market trading, there are still some issues in the
existing prediction methods, including the processing of data noise, inexplicable selection of …