Web: http://arxiv.org/abs/2209.10662

Sept. 23, 2022, 1:11 a.m. | Yitian Zhang, Florence Regol, Antonios Valkanas, Mark Coates

cs.LG updates on arXiv.org arxiv.org

There have been several recent efforts towards developing representations for
multivariate time-series in an unsupervised learning framework. Such
representations can prove beneficial in tasks such as activity recognition,
health monitoring, and anomaly detection. In this paper, we consider a setting
where we observe time-series at each node in a dynamic graph. We propose a
framework called GraphTNC for unsupervised learning of joint representations of
the graph and the time-series. Our approach employs a contrastive learning
strategy. Based on an assumption …

arxiv graphs series time series

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