Gross error detection and correction based on wavelet transform and support vector machine

Tingye Tao; Fei Gao; Zhaofu Wu

doi:10.1117/12.838574

14 October 2009 Gross error detection and correction based on wavelet transform and support vector machine

Tingye Tao, Fei Gao, Zhaofu Wu

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74921E (2009) https://doi.org/10.1117/12.838574
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

In order to obtain high accuracy results, the gross errors in observations must be correctly detected and repaired. In this paper, the theory and methods of singularity detection based on wavelet transform, support vector machine regression model are introduced. The wavelet multi-resolution analysis (MRA) was carried out and the location of the gross errors can be detected by ascertaining the points of modulus maximal value of the wavelet coefficients since the gross error can be regarded as the singular point of the observation time series. Then the time series regression model based on support vector machine (SVM) was established to repair the gross errors. Practical test results indicate that the gross errors can be validly detected by wavelet method as well as be correctly repaired by the method based on support vector machine.

Citation Download Citation

Tingye Tao, Fei Gao, and Zhaofu Wu "Gross error detection and correction based on wavelet transform and support vector machine", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921E (14 October 2009); https://doi.org/10.1117/12.838574

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