Adaptive Outlier-Tolerant Predictors of Controlled Autoregressive Process

Abstract:

The controlled autoregressive model is widely used to describe a dynamic process. In this paper, a series of new algorithms are proposed to estimate the models coefficients and to predict future change of the process. On the one hand, it is can be proved that these new algorithms are outlier-tolerant in the case that there are outliers in sampling series. On the one hand, these new algorithms are near to the optimal estimators and predictors separately in normal case.

• Periodical: Applied Mechanics and Materials (Volumes 321-324)
• Pages: 829-832
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.321-324.829
• Online since: June 2013
• Authors: Shao Lin Hu, Yue Yang, Meng Li
• Keywords: Controlled Autoregressive Process, Diagnosis, Outlier-Tolerant Prediction

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