Wavelet Based Outlier Correction for Power Controlled Turning Point Detection in Surveillance Systems
AbstractDetection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.
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Bibliographic InfoPaper provided by Lund University, Department of Economics in its series Working Papers with number 2012:12.
Length: 15 pages
Date of creation: 21 May 2012
Date of revision:
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Unimodel; Turning point; Statistical Surveillance; Outlier; Wavelet multiresolution; Threshold;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
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- Ling, Shiqing & Li, W.K., 2003. "Asymptotic Inference For Unit Root Processes With Garch(1,1) Errors," Econometric Theory, Cambridge University Press, vol. 19(04), pages 541-564, August.
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