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Activity Number: 15 - Networks, Multivariate Analysis, and Time Series
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Royal Statistical Society
Abstract #322959 View Presentation
Title: Network Time Series Modeling
Author(s): Marina Knight* and Matthew Nunes and Guy Nason
Companies: University of York and Lancaster University and University of Bristol
Keywords: autoregressive time series ; complex networks ; wavelets

A network time series is a multivariate time series augmented by a graph that describes how variables (or nodes) are connected. We introduce the network autoregressive (integrated) moving average (NARIMA) processes: a set of flexible models for network time series. For fixed networks the NARIMA models are essentially equivalent to vector autoregressive moving average-type models. However, NARIMA models are especially useful when the structure of the graph, associated with the multivariate time series, changes over time. Such network topology changes are invisible to standard VARMA-like models. For integrated NARIMA models we introduce network differencing, based on the network lifting (wavelet) transform, which removes trend. We exhibit our techniques on a network time series describing the evolution of mumps throughout counties of England and Wales weekly during 2005.

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