Title of dissertation: Scalable Techniques for Behavioral Analysis and Forecasting Amy Sliva, Doctor of Philosophy, 2011 Dissertation directed by: Professor V.S. Subrahmanian Department of Computer Science The ability to model, forecast, and analyze the behaviors of other agents has applications in many diverse contexts. For example, behavioral models can be used in multi-player games to foreca...

The paper presents a modelling methodology for small scale multi-source renewable energy systems. Using historical site-specific weather data, the relationships of cost, availability and energy form are visualised as a function of the sizing of photovoltaic arrays, wind turbines, and battery capacity. The specific dependency of each site on its own particular weather patterns show that unique s...

The earned value management (EVM) model is an essential technique for managing and forecasting project features such as scheduling and cost performances indexes. This paper presents a novel fuzzy earned-value model based on Z-number theory incorporating both the impreciseness of real life conditions and a degree of reliability through considering an expert judgment process. The latter factor ha...

Advanced analytic and forecasting methodologies can enable organisations to more fully leverage the data resources available to them. In the healthcare industry, service providers can use data mining methods to enhance the decision-making process in optimising resource allocation by identifying the sources of future high-cost treatment in a given health plan population. The following paper incl...

The M3-Competition continues to improve the design of forecasting competitions: It examines more series than any previous competition, improves error analyses. and includes commercial forecasting programs as competitors. To judge where to go from here, I step back to look at the M-Competitions as a whole. I discuss the advantages of the M-Competitions in hopes that they will be retained, descri...

Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting subsystem which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed events. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the be...

INTRODUCTION An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. METHODOLOGY AND PRINCIPAL FINDINGS We developed a time series Poisson multivariate regression mod...

This paper proposes a decentralized control technique to minimize the total operation cost of a DC microgrid in both grid-connected and islanded modes. In this study, a cost-based droop control scheme based on the hourly bids of all participant distributed generators (DGs) and the hourly energy price of the utility is presented. An economic power sharing technique among various types of DG unit...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

This paper compares alternative time-series models to forecast state tax revenues. Forecast accuracy is compared to a benchmark random walk forecast. Quarterly data for California is used to forecast total tax revenue along with its three largest components, sales, income, and corporate tax revenue. For oneand four-quarter-ahead forecasts from 2004 to 2009, Bayesian vector autoregressions gener...