This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and...

Time-series forecasting is essential for system analysis. Many traditional studies have paid attention to individual stock-oriented solutions and disregarded general approaches on financial time series or skipped the dynamics of the system and its triggering components. It is difficult to fully...

Forecasting container throughput is critical for improved port planning, operations, and investment strategies. Reliability of forecasting methods need to be ensured before utilizing their outcomes in decision making. This study compares forecasting performances of various time series methods,...

To overcome the curse of dimensionality, dimension reduction is important andnecessary for understanding the underlying phenomena in a variety of fields.Dimension reduction is the transformation of high-dimensional data into ameaningful representation in the low-dimensional space. It can be...