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
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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 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: | https://discovery.ucl.ac.uk/id/eprint/1474744 |
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