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SUMMARY:Network Time Series - Prof Guy Nason\, University of Bristol
DTSTART:20170921T133000Z
DTEND:20170921T143000Z
UID:TALK81611@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:A network time series is a multivariate time series where the 
 individual series are known to be linked by some underlying network struct
 ure. Sometimes this network is known a priori\, and sometimes the network 
 has to be inferred\, often from the multivariate series itself. Network ti
 me series are becoming increasingly common\, long\, and collected over a l
 arge number of variables. We are particularly interested in network time s
 eries whose network structure changes over time.\n\nWe describe some recen
 t developments in the modeling and analysis of network time series via net
 work autoregressive integrated moving average (NARIMA) process models. NAR
 IMA models provide a network extension to a familiar environment that can 
 be used to extract valuable information and aid prediction. As with classi
 cal ARIMA models\, trend can impair the estimation of NARIMA parameters. T
 he scope for trend removal is somewhat wider with NARIMA models and we exh
 ibit some possibilities.\n\nWe will illustrate the prototypical operation 
 of NARIMA modeling on data sets arising from human and veterinary epidemio
 logy.\n\nThis is joint work with Kathryn Leeming (Bristol)\, Marina Knight
  (York) and Matt Nunes (Lancaster).
LOCATION:Large Seminar Room\, 1st Floor\, Institute of Public Health\, Uni
 versity Forvie Site\, Robinson Way\, Cambridge
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