Title :
Dynamic modeling and forecasting on enterprise revenue with derived granularities
Author :
Shan, Jerry Z. ; Tang, Hsiu-Khuem ; Wu, Ren ; Safai, Fereydoon
Author_Institution :
HP Labs., Hewlett-Packard Co., CA, USA
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.
Keywords :
belief networks; decision making; economic forecasting; financial management; forecasting theory; inference mechanisms; Bayesian inference; decision-making processes; derived data granularity; dynamic forecasting; dynamic modeling; enterprise revenue; predictive reporting system; seasonal ARIMA models; Business; Companies; Data warehouses; Decision making; Geography; History; Monitoring; Predictive models; Stochastic processes; Target tracking; Bayesian inference; data granularity; modeling and forecasting; seasonal ARIMA models;
Conference_Titel :
Granular Computing, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9017-2
DOI :
10.1109/GRC.2005.1547276
