Performance prediction for Grid workflow activities based on features-ranked RBF network

Accurate performance prediction of Grid workflow activities can help Grid schedulers map activities to appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural network to predict the performance of Grid workflow activities.Experimental results for two kinds of real world Grid workflow activities are presented to show effectiveness of our approach.     

The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach

This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.