Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …

The most important problems with exploiting artificial neural networks (ANNs) are to design
the network topology, which usually requires an excessive amount of expert's effort, and to …

Abstract Financial Time Series Prediction is a complex and a challenging problem. In this
paper, we propose two 3-stage hybrid prediction models wherein Chaos theory is used to …

Corporate credit rating assessment is one of the crucial problems of credit risk management;
it will help the financial institutions and government decide whether to issue debts. Recent …

Student attrition–the departure from an institution of higher learning prior to the achievement
of a degree or earning due educational credentials–is an administratively important …

Effective and timely identification of working conditions is critical for ensuring safe and
efficient long-term operations in industrial flotation processes. Previous studies on the …

The aerospace, automotive and marine industries have witnessed a rapid increase of using
adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering …

In prognostics and health management (PHM), the sensor measurement time series of
equipment is collected, and predicting future sensor measurements accurately is crucial to …

In the class imbalanced learning scenario, traditional machine learning algorithms focusing
on optimizing the overall accuracy tend to achieve poor classification performance …