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Proceedings Paper

Tracking signal test to monitor an intelligent time series forecasting model
Author(s): Yan Deng; Majid Jaraiedi; Wafik H. Iskander
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Paper Abstract

Extensive research has been conducted on the subject of Intelligent Time Series forecasting, including many variations on the use of neural networks. However, investigation of model adequacy over time, after the training processes is completed, remains to be fully explored. In this paper we demonstrate a how a smoothed error tracking signals test can be incorporated into a neuro-fuzzy model to monitor the forecasting process and as a statistical measure for keeping the forecasting model up-to-date. The proposed monitoring procedure is effective in the detection of nonrandom changes, due to model inadequacy or lack of unbiasedness in the estimation of model parameters and deviations from the existing patterns. This powerful detection device will result in improved forecast accuracy in the long run. An example data set has been used to demonstrate the application of the proposed method.

Paper Details

Date Published: 4 March 2004
PDF: 12 pages
Proc. SPIE 5263, Intelligent Manufacturing, (4 March 2004); doi: 10.1117/12.517225
Show Author Affiliations
Yan Deng, West Virginia Univ. (United States)
Majid Jaraiedi, West Virginia Univ. (United States)
Wafik H. Iskander, West Virginia Univ. (United States)

Published in SPIE Proceedings Vol. 5263:
Intelligent Manufacturing
Bhaskaran Gopalakrishnan; Peter E. Orban; Angappa Gunasekaran, Editor(s)

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