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Signal processing over networks

Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series


Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes.

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Authors:
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano
Submitted On:
1 March 2019 - 9:08pm
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[1] Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3841. Accessed: Mar. 23, 2019.
@article{3841-18,
url = {http://sigport.org/3841},
author = {Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano },
publisher = {IEEE SigPort},
title = {Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series},
year = {2018} }
TY - EJOUR
T1 - Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series
AU - Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3841
ER -
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. IEEE SigPort. http://sigport.org/3841
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, 2018. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. Available at: http://sigport.org/3841.
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series." Web.
1. Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3841