Tracking signal test to monitor an intelligent time series forecasting model

Yan Deng; Majid Jaraiedi; Wafik H. Iskander

doi:10.1117/12.517225

4 March 2004 Tracking signal test to monitor an intelligent time series forecasting model

Yan Deng, Majid Jaraiedi, Wafik H. Iskander

Proceedings Volume 5263, Intelligent Manufacturing; (2004) https://doi.org/10.1117/12.517225
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States

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.

Citation Download Citation

Yan Deng, Majid Jaraiedi, and Wafik H. Iskander "Tracking signal test to monitor an intelligent time series forecasting model", Proc. SPIE 5263, Intelligent Manufacturing, (4 March 2004); https://doi.org/10.1117/12.517225

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available