Standard
A power variance test for nonstationarity in complex-valued signals. / Bartlett, Thomas E.
; Sykulski, Adam M.; Olhede, Sofia C. et al.
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/ISSN › Conference contribution/Paper › peer-review
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.2015.122APA
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). Article 7424437 Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/ICMLA.2015.122Vancouver
Author
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 -
