Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Estimating Dynamic Graphical Models from Multivariate Time-Series Data : Recent Methods and Results. / Gibberd, A. J.; Nelson, J. D. B.
Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers. ed. / Ahlame Douzal-Chouakria; José A. Vilar; Pierre-François Marteau. Cham : Springer International Publishing AG, 2016. p. 111-128.Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Estimating Dynamic Graphical Models from Multivariate Time-Series Data
T2 - Recent Methods and Results
AU - Gibberd, A. J.
AU - Nelson, J. D. B.
PY - 2016
Y1 - 2016
N2 - Dynamic graphical models aim to describe the time-varying dependency structure of multiple time-series. In this article we review research focusing on the formulation and estimation of such models. The bulk of work in graphical structurelearning problems has focused in the stationary i.i.d setting, we present a brief overview of this work before introducing some dynamic extensions. In particular we focuson two classes of dynamic graphical model; continuous (smooth) models which are estimated via localised kernels, and piecewise models utilising regularisation based estimation. We give an overview of theoretical and empirical results regarding these models, before demonstrating their qualitative difference in the context of a real-world financial time-series dataset. We conclude with a discussion of the state of the field and future research directions.
AB - Dynamic graphical models aim to describe the time-varying dependency structure of multiple time-series. In this article we review research focusing on the formulation and estimation of such models. The bulk of work in graphical structurelearning problems has focused in the stationary i.i.d setting, we present a brief overview of this work before introducing some dynamic extensions. In particular we focuson two classes of dynamic graphical model; continuous (smooth) models which are estimated via localised kernels, and piecewise models utilising regularisation based estimation. We give an overview of theoretical and empirical results regarding these models, before demonstrating their qualitative difference in the context of a real-world financial time-series dataset. We conclude with a discussion of the state of the field and future research directions.
KW - Graphical model
KW - Sparsity
KW - Changepoint
KW - Time-series
KW - Dynamics
KW - Regularization
U2 - 10.1007/978-3-319-44412-3_8
DO - 10.1007/978-3-319-44412-3_8
M3 - Conference contribution/Paper
SN - 9783319444123
SP - 111
EP - 128
BT - Advanced Analysis and Learning on Temporal Data: First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
A2 - Douzal-Chouakria, Ahlame
A2 - Vilar, José A.
A2 - Marteau, Pierre-François
PB - Springer International Publishing AG
CY - Cham
ER -