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A Power Variance Test for Nonstationarity in Complex-Valued Signals

Bartlett, TE; Sykulski, AM; Olhede, SC; Lilly, JM; Early, JJ; (2016) A Power Variance Test for Nonstationarity in Complex-Valued Signals. In: Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on. (pp. pp. 911-916). IEEE Green open access

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Abstract

We propose a novel algorithm for testing the hypothesis of nonstationarity in complex-valued signals. The implementation uses both the bootstrap and the Fast Fourier Transform such that the algorithm can be efficiently implemented in O(NlogN) time, where N is the length of the observed signal. The test procedure examines the second-order structure and contrasts the observed power variance - i.e. the variability of the instantaneous variance over time - with the expected characteristics of stationary signals generated via the bootstrap method. Our algorithmic procedure is capable of learning different types of nonstationarity, such as jumps or strong sinusoidal components. We illustrate the utility of our test and algorithm through application to turbulent flow data from fluid dynamics.

Type: Proceedings paper
Title: A Power Variance Test for Nonstationarity in Complex-Valued Signals
Event: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, 2015, pp. 911-916.
ISBN-13: 978-1-5090-0287-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICMLA.2015.122
Publisher version: https://doi.org/10.1109/ICMLA.2015.122
Language: English
Additional information: Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: oceanography, Stochastic processes, time-series analysis, nonstationary processes, bootstrap
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1474744
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