A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT)

A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT) Electronic Data Storage Tags (DSTs) have the potential of providing long term, high-resolution observation data of individual fish in the ecosystem. However, traditional time series analysis methods are limited in extracting the required information from DSTs. The continuous wavelet transform (CWT) is a tool for decomposing a time series into instantaneous frequency versus time characteristics. This allows data signals, which are restricted to specific frequencies (localized frequencies), or frequency components, which are restricted to specific time intervals (time localized) to be extracted and displayed in a highly intuitive way. The aim of this short communication is to present the CWT as a tool for extracting information from DST data. In the first case, synthetically generated data is used to demonstrate how the methodology captures different time–frequency dependent patterns in the time series data. The relevance of the methodology to real field data is demonstrated by an analysis of DST depth data from tagged Barents Sea coastal cod. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reviews in Fish Biology and Fisheries Springer Journals

A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT)

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Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer Science+Business Media B.V.
Subject
Life Sciences; Zoology ; Freshwater & Marine Ecology
ISSN
0960-3166
eISSN
1573-5184
D.O.I.
10.1007/s11160-007-9078-2
Publisher site
See Article on Publisher Site

Abstract

Electronic Data Storage Tags (DSTs) have the potential of providing long term, high-resolution observation data of individual fish in the ecosystem. However, traditional time series analysis methods are limited in extracting the required information from DSTs. The continuous wavelet transform (CWT) is a tool for decomposing a time series into instantaneous frequency versus time characteristics. This allows data signals, which are restricted to specific frequencies (localized frequencies), or frequency components, which are restricted to specific time intervals (time localized) to be extracted and displayed in a highly intuitive way. The aim of this short communication is to present the CWT as a tool for extracting information from DST data. In the first case, synthetically generated data is used to demonstrate how the methodology captures different time–frequency dependent patterns in the time series data. The relevance of the methodology to real field data is demonstrated by an analysis of DST depth data from tagged Barents Sea coastal cod.

Journal

Reviews in Fish Biology and FisheriesSpringer Journals

Published: Nov 22, 2007

References

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