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| Received:November 12, 2009Revised:March 12, 2010 |
| 基金项目:This work was supported by the National Science Foundation for Distinguished Young Scholars of China (No. 60925011). |
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| Estimation of time series noise covariance using correlation technology |
| Tao MA,Jie CHEN,Wenjie CHEN,Zhihong PENG |
| (School of Automation, Beijing Institute of Technology; Key Laboratory of Complex System Intelligent Control and Decision (Ministry of Education), Beijing Institute of Technology) |
| Abstract: |
| Covariance of clean signal and observed noise is necessary for extracting clean signal from a time series. This is transferred to calculate the covariance of observed noise and clean signal’sMA process, when the clean signal is described by an autoregressive moving average (ARMA) model. Using the correlations of the innovations data from observed time series to form a least-squares problem, a concisely autocovariance least-square (CALS) method has been proposed to estimate the covariance.We also extended our work to the case of unknown MA process coefficients. Comparisons between Odelson’s autocovariance least-square (ALS) estimation algorithm and the proposed CALS method show that the CALS method could get a much more exact and compact estimation of the covariance than ALS and its extended form. |
| Key words: Time series Correlation technology Covariance estimation Least-square method |
