In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Mediano:2021:10.1103/physrevlett.127.124101,
author = {Mediano, PAM and Rosas, FE and Barrett, AB and Bor, D},
doi = {10.1103/physrevlett.127.124101},
journal = {Physical Review Letters},
pages = {1--4},
title = {Decomposing spectral and phasic differences in nonlinear features between datasets},
url = {http://dx.doi.org/10.1103/physrevlett.127.124101},
volume = {127},
year = {2021}
}
TY - JOUR
AB - When employing nonlinear methods to characterize complex systems, it is important to determine to what extent they are capturing genuine nonlinear phenomena that could not be assessed by simpler spectral methods. Specifically, we are concerned with the problem of quantifying spectral and phasic effects on an observed difference in a nonlinear feature between two systems (or two states of the same system). Here we derive, from a sequence of null models, a decomposition of the difference in an observable into spectral, phasic, and spectrum-phase interaction components. Our approach makes no assumptions about the structure of the data and adds nuance to a wide range of time series analyses.
AU - Mediano,PAM
AU - Rosas,FE
AU - Barrett,AB
AU - Bor,D
DO - 10.1103/physrevlett.127.124101
EP - 4
PY - 2021///
SN - 0031-9007
SP - 1
TI - Decomposing spectral and phasic differences in nonlinear features between datasets
T2 - Physical Review Letters
UR - http://dx.doi.org/10.1103/physrevlett.127.124101
UR - https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.124101
UR - http://hdl.handle.net/10044/1/103106
VL - 127
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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