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A power variance test for nonstationarity in complex-valued signals

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A power variance test for nonstationarity in complex-valued signals. / Bartlett, Thomas E.; Sykulski, Adam M.; Olhede, Sofia C.; Lilly, Jonathan M.; Early, Jeffrey J.

Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 911-916 7424437.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Bartlett, TE, Sykulski, AM, Olhede, SC, Lilly, JM & Early, JJ 2015, A power variance test for nonstationarity in complex-valued signals. in Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015., 7424437, Institute of Electrical and Electronics Engineers Inc., pp. 911-916, IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Miami, United States, 9/12/15. https://doi.org/10.1109/ICMLA.2015.122

APA

Bartlett, T. E., Sykulski, A. M., Olhede, S. C., Lilly, J. M., & Early, J. J. (2015). A power variance test for nonstationarity in complex-valued signals. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (pp. 911-916). [7424437] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2015.122

Vancouver

Bartlett TE, Sykulski AM, Olhede SC, Lilly JM, Early JJ. A power variance test for nonstationarity in complex-valued signals. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 911-916. 7424437 https://doi.org/10.1109/ICMLA.2015.122

Author

Bartlett, Thomas E. ; Sykulski, Adam M. ; Olhede, Sofia C. ; Lilly, Jonathan M. ; Early, Jeffrey J. / A power variance test for nonstationarity in complex-valued signals. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 911-916

Bibtex

@inproceedings{b37ac8506fc34d9fb2a235110823d26f,
title = "A power variance test for nonstationarity in complex-valued signals",
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.",
keywords = "Bootstrap, Nonstationary processes, Oceanography, Stochastic processes, Time-series analysis",
author = "Bartlett, {Thomas E.} and Sykulski, {Adam M.} and Olhede, {Sofia C.} and Lilly, {Jonathan M.} and Early, {Jeffrey J.}",
year = "2015",
doi = "10.1109/ICMLA.2015.122",
language = "English",
pages = "911--916",
booktitle = "Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 ; Conference date: 09-12-2015 Through 11-12-2015",

}

RIS

TY - GEN

T1 - A power variance test for nonstationarity in complex-valued signals

AU - Bartlett, Thomas E.

AU - Sykulski, Adam M.

AU - Olhede, Sofia C.

AU - Lilly, Jonathan M.

AU - Early, Jeffrey J.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Bootstrap

KW - Nonstationary processes

KW - Oceanography

KW - Stochastic processes

KW - Time-series analysis

U2 - 10.1109/ICMLA.2015.122

DO - 10.1109/ICMLA.2015.122

M3 - Conference contribution/Paper

AN - SCOPUS:84969677221

SP - 911

EP - 916

BT - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Y2 - 9 December 2015 through 11 December 2015

ER -