TY - GEN
T1 - Predicting the evolution of stationary graph signals
AU - Loukas, Andreas
AU - Isufifi, Elvin
AU - Perraudin, Nathanael
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - One way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph. We here focus on the problem of predicting the evolution of a process that is time and graph stationary, i.e., a time-varying signal whose first two statistical moments are invariant over time and correlated to a known graph topology. This stationarity assumption allows us to regularize the estimation problem, reducing the variance and computational complexity, two common issues plaguing high-dimensional vector autoregressive models. In addition, our method compares favorably to state-of-the-art graph and time-based methods: it outperforms previous graph causal models as well as a purely time-based method.
AB - One way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph. We here focus on the problem of predicting the evolution of a process that is time and graph stationary, i.e., a time-varying signal whose first two statistical moments are invariant over time and correlated to a known graph topology. This stationarity assumption allows us to regularize the estimation problem, reducing the variance and computational complexity, two common issues plaguing high-dimensional vector autoregressive models. In addition, our method compares favorably to state-of-the-art graph and time-based methods: it outperforms previous graph causal models as well as a purely time-based method.
KW - Signal processing on graphs
KW - joint stationarity
KW - multivariate processes
KW - prediction
KW - time-varying graph signals
UR - http://www.scopus.com/inward/record.url?scp=85050948256&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2017.8335136
DO - 10.1109/ACSSC.2017.8335136
M3 - Conference contribution
AN - SCOPUS:85050948256
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 60
EP - 64
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -