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Dynamic Modeling and Forecasting on Enterprise Revenue with Derived Granularities

Shan, Jerry Z.; Tang, Hsiu-Khuern; Wu, Ren; Safai, Fereydoon


Keyword(s): Bayesian inference; data granularity; modeling and forecasting; seasonal ARIMA models

Abstract: Timely and accurate forecasts are crucial in decision-making processes and have significant impacts on many business aspects. We at HP Labs have developed a complete set of quantitative forecasting methods that can enable the establishment of a reliable predictive reporting system, so that executives can discern as early as possible where the company is heading financially. This paper reports some of our technical developments in building such a predictive reporting system. Notes: Copyright IEEE. To be published in and presented at IEEE International Conference on Granular Computing (IEEE GrC 2005), 25- 27 July 2005, Beijing, China

6 Pages

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