With the increasing number of geographically dis­ tributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency o...

One of the major difficulties introduced with wind power penetration is the inherent uncertainty in production originating from uncertain wind conditions. This uncertainty impacts many different aspects of power system operation, especially the balancing power requirements. For this reason, in power system development planing, it is necessary to evaluate the potential uncertainty in future wind...

Our long term goal is to better understand ensemble forecasting in general and the dynamics of initial uncertainty given only imperfect models in particular; we are most interested in the practical application to a real physical system, rather than toy examples where the model error is known a priori. Of particular interest to us are methods which aim to evaluate predictions of high dimensional...

J O H N C . R O B E R T S O N A N D E L L I S W . T A L L M A N Robertson is a visiting scholar and Tallman is a senior economist in the macropolicy section of the Atlanta Fed’s research department. They thank Lucy Ackert and Mary Rosenbaum for comments, Robert Parker and Bruce Grimm of the BEA for the NIPA time series data and comments and insights into the bureau’s data revision process, Glen...

Abstract The linear multiregression dynamic model (LMDM) is a Bayesian dynamic model which preserves any conditional independence and causal structure across a multivariate time series. The conditional independence structure is used to model the multivariate series by separate (conditional) univariate dynamic linear models, where each series has contemporaneous variables as regressors in its mo...

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We the error as function of features calculated from historical with an efficient Bayesian multivariate surface regression approach. The minimum predicted is then used to identify individual or combination models produce final forecasts. It well known that most depends on representativen...

the purpose of this study is presenting a model forecasting financial crisis in tehran stock exchange listed companies. to do this, productive firms that had been accepted in tehran stock exchange between 2002 and 2009, were selected as the study sample. first the independent variables were obtained based on financial ratios and then based on article 141 of the law of commerce, the insolvent an...