This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we de...

A computer model is built to simulate master production scheduling activities in a capacitated multi-item production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and th...

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accur...

Research of short-term load forecasting has important practical application value in the field of power network dispatching. The regession models of least squares support vector machines (LS-SVM) have been applied to load forecasting field widely, and the regression accuracy and generalization performance of the LS-SVM models depend on a proper selection of its parameters. In this paper, a new ...

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeco...

In this study, we propose a novel nonlinear ensemble forecasting model integrating generalized linear autoregression (GLAR) with artificial neural networks (ANN) in order to obtain accurate prediction results and ameliorate forecasting performances. We compare the new model’s performance with the two individual forecasting models—GLAR and ANN—as well as with the hybrid model and the linear comb...

In two studies, the authors examined whether people who are high in emotional intelligence (EI) make more accurate forecasts about their own affective responses to future events. All participants completed a performance measure of EI (the Mayer-Salovey-Caruso Emotional Intelligence Test) as well as a self-report measure of EI. Affective forecasting ability was assessed using a longitudinal desi...

Student performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate p...

Recently, ensemble techniques have also attracted the attention of Genetic Programing (GP) researchers. The goal is to further improve GP classification performances. Among the ensemble techniques, also bagging and boosting have been taken into account. These techniques improve classification accuracy by combining the responses of different classifiers by using a majority vote rule. However, it...