Visualization and inference based on wavelet coefficients, SiZer and SiNos

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SiZer (Significant ZERo crossing of the derivatives) and SiNos (Slgnificant NOn-Stationarities) are scale-space based visualization tools for statistical inference. They are used to discover meaningful structure in data through exploratory analysis involving statistical smoothing techniques. Wavelet methods have been successfully used to analyze various types of time series. In this paper, we propose a new time series analysis approach, which combines the wavelet analysis with the visualization tools SiZer and SiNos. We use certain functions of wavelet coefficients at different scales as inputs, and then apply SiZer or SiNos to highlight potential non-stationarities. We show that this new methodology can reveal hidden local non-stationary behavior of time series, that are otherwise difficult to detect. (C) 2006 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER
Issue Date
2007-08
Language
English
Article Type
Article
Citation

COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.51, no.12, pp.5994 - 6012

ISSN
0167-9473
DOI
10.1016/j.csda.2006.11.037
URI
http://hdl.handle.net/10203/285784
Appears in Collection
MA-Journal Papers(저널논문)
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