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Activity Number: 583 - Learning Network Structure in Heterogeneous Populations
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #312296
Title: Modeling Network Time Series Using Generalized Network AutoRegression (GNAR)
Author(s): Kathryn Leeming* and Marina Knight and Guy Nason and Matthew Nunes
Companies: University of Warwick and University of York and Imperial College London and University of Bath
Keywords: Time series; Network

Network time series consist of time series recorded at nodes of a network. This network describes the relationship between the individual time series, and may feature direction or weight information. Such networks could be physical, social, or geographical, depending on the particular data application. Our GNAR model describes autoregressive behaviour of the time series both within and between nodes parsimoniously, and has been shown to outperform the VAR model in prediction tasks. The GNAR model can also capture differences in behaviour within the network, such as in different communities. A motivating environmental example will be presented, as well as possible extensions to this model.

Authors who are presenting talks have a * after their name.

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