Abstract
During preceding theory study and engineering application, we dealed with the parameter estimation of one-dimension long memory process actually, and rarely take into account high dimensions. There are few papers about it. In this paper, using the decorrelation property of discrete wavelet transform, high dimension situation (mainly 2D) is simplified to 1D and corresponding referrers are improved according to new idea, combining with matrix transform. So the computation complexity is reduced effectively and estimation precision is satisfied. Some experiment results show that this algorithm has a better general performance.
This work is partially supported by NSFC (60434020, 60374020), International Cooperative Project Foundation (0446650006), Henan Outstanding Youth Science Fund (0312001900), and Ministry of Education Science Foundation (205092).
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Wen, CL., Wang, GJ., Wen, CB., Chen, ZG. (2007). Multiscale Estimation to the Parameter of Multidimension Time Series. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_95
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DOI: https://doi.org/10.1007/978-3-540-72395-0_95
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