Generalized Network Autoregressive Processes and the <b>GNAR</b> Package

Knight, Marina I.; Leeming, Kathryn; Nason, Guy P.; Nunes, Matthew

doi:10.18637/jss.v096.i05

Cited by 31 publications

(26 citation statements)

References 19 publications

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“…As these clusters vary daily, the subset of common subgroups of regions that appears at least 75% of the times in the same cluster are retained to form the final MFAs. The future work is to introduce this correlation structure into the anomaly detection system via Generalized Network AutoRegressive processes (GNAR) in the spirit of Knight et al (2020) and Dahlhaus and Eichler (2003), but tailored for the high dimensionality of our setup.…”

Section: Classification Of the Severity Of Signalsmentioning

confidence: 99%

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Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

Iacus

Sermi

Spyratos

et al. 2021

Jpn J Stat Data Sci

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Due to an unprecedented agreement with the European Mobile Network Operators, the Joint Research Centre of the European Commission was in charge of collecting and analyze mobile positioning data to provide scientific evidence to policy makers to face the COVID-19 pandemic. This work introduces a live anomaly detection system for these high-frequency and high-dimensional data collected at European scale. To take into account the different granularity in time and space of the data, the system has been designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. A web application designed for policy makers, makes possible to visualize the anomalies and perceive the effect of containment and lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings.

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Section: Classification Of the Severity Of Signalsmentioning

confidence: 99%

“…As mentioned in Sect. 3, future direction of investigation will consider the introduction into the anomaly detection system of Generalized Network AutoRegressive processes (GNAR) (2003;Knight et al 2020).…”

Section: Conclusion and Limits Of This Approachmentioning

confidence: 99%

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

Iacus

Sermi

Spyratos

et al. 2021

Jpn J Stat Data Sci

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“…In this section, first we describe the Generalized Network Autoregressive Process (GNAR) [18] and the associated R package [20]. Then, we present the way that we adapt the GNAR model to our problem.…”

Section: The Generalized Network Autoregressive Processmentioning

confidence: 99%

SARS-CoV-2 Dissemination using a Network of the United States Counties

Urrutia¹,

Wren²,

Vogiatzis³

et al. 2021

Preprint

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During 2020 and 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been increasing amongst the world's population at an alarming rate. Reducing the spread of SARS-CoV-2 and other diseases that are spread in similar manners is paramount for public health officials as they seek to effectively manage resources and potential population control measures such as social distancing and quarantines. By analyzing the United States' county network structure, one can model and interdict potential higher infection areas. County officials can provide targeted information, preparedness training, as well as increase testing in these areas. While these approaches may provide adequate countermeasures for localized areas, they are inadequate for the holistic United States. We solve this problem by collecting coronavirus disease 2019 (COVID-19) infections and deaths from the Center for Disease Control and Prevention and a network adjacency structure from the United States Census Bureau. Generalized network autoregressive (GNAR) time series models have been proposed as an efficient learning algo-

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“…In parallel, highdimensional time series datasets fostered the development of sparse inference for OU-type processes (Boninsegna et al 2018;Gaïffas and Matulewicz 2019) as a way to control interactions within complex systems. On the other hand, graphical time series models are usually restricted to discrete-time time series (Knight et al 2016(Knight et al , 2020Zhu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Likelihood theory for the graph Ornstein-Uhlenbeck process

Courgeau

Veraart

2021

Stat Inference Stoch Process

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We consider the problem of modelling restricted interactions between continuously-observed time series as given by a known static graph (or network) structure. For this purpose, we define a parametric multivariate Graph Ornstein-Uhlenbeck (GrOU) process driven by a general Lévy process to study the momentum and network effects amongst nodes, effects that quantify the impact of a node on itself and that of its neighbours, respectively. We derive the maximum likelihood estimators (MLEs) and their usual properties (existence, uniqueness and efficiency) along with their asymptotic normality and consistency. Additionally, an Adaptive Lasso approach, or a penalised likelihood scheme, infers both the graph structure along with the GrOU parameters concurrently and is shown to satisfy similar properties. Finally, we show that the asymptotic theory extends to the case when stochastic volatility modulation of the driving Lévy process is considered.

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Generalized Network Autoregressive Processes and the GNAR Package

Cited by 31 publications

References 19 publications

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

SARS-CoV-2 Dissemination using a Network of the United States Counties

Likelihood theory for the graph Ornstein-Uhlenbeck process

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