For market players and policy officials, commodity price forecasts are crucial problems that
are challenging to address due to the complexity of price time series. Given its strategic …

Financial time series forecasting is a difficult task due to the complexity and volatility of
financial markets. Machine learning models have been applied to tackle this task, but finding …

In the last few decades, metaheuristic algorithms that use the laws of nature have been used
dramatically in numerous and complex optimization problems. The artificial hummingbird …

Hydrological modelling can be complex in nonhomogeneous catchments with diverse
geological, climatic, and topographic conditions. In this study, an integrated conceptual …

Accurate measurements of available water resources play a key role in achieving a
sustainable environment of a society. Precise river flow estimation is an essential task for …

Stock market plays a vital role in a country's economy, serving as a platform for companies to
raise capital and enabling investors to share in their growth and success. The market is very …

To achieve smart and sustainable construction goals, machine learning (ML) techniques can
serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ …

The stock market is changing quickly, and its nonlinear characteristics make stock price
prediction difficult. Predicting stock prices is challenging due to several factors, including a …

As communication technologies and equipment evolve, smart assets become smarter. The
agricultural industry is also evolving in line with the implementation of modern …

Accurate runoff forecasting is of great significance for water resource allocation flood control
and disaster reduction. However, due to the inherent strong randomness of runoff …