This research focused on improving estimations of demand functions using information from a large number of related many-period time series. The key premise is that by improving the data's internal consistency random variation will be reduced in the component time series. The research starts with the assumption that some of the demand time series are significantly correlated, some only slightly correlated, and some completely uncorrelated. A process is then developed for improving the demand function of any distinct part by using the correlated demands of the other parts. Metrics of effectiveness are proposed and used to test the methodology. The resulting tests demonstrate the effectiveness of the method, referred to as an internally consistent correlations (ICC) method. Importantly, the research makes no use of a clustering algorithm and no assumption about the "end use" of the data set. Thus, it is expected that the results will apply to a wide array of domains, including forecasting, data mining, signal processing, and process monitoring.
63 REFINING UNDERLYING DEMAND FUNCTIONS USING INFORMATION FROM RELATED TIME SERIES Glenn E. Maples, University of Louisiana at Lafayette Ronald B. Heady, University of Louisiana at Lafayette Zhiwei Zhu, University of Louisiana at Lafayette ABSTRACT This research f
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