Asynchronous Graph Generators
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: continuous-time dynamic graphs, time-series imputation, graph neural networks, graph generators
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TL;DR: A methodology for considering multichannel time series data as asynchronous graphs, naturally handling sparse, incomplete time series representations.
Abstract: We introduce the asynchronous graph generator (AGG), a novel graph neural network architecture for multi-channel time series which models observations as nodes on a dynamic graph and can thus perform data imputation by transductive node generation. Completely free from recurrent components or assumptions about temporal regularity, AGG represents measurements, timestamps and metadata directly in the nodes via learnable embeddings, to then leverage attention to learn expressive relationships across the variables of interest. This way, the proposed architecture implicitly learns a causal graph representation of sensor measurements which can be conditioned on unseen timestamps and metadata to predict new measurements by an expansion of the learnt graph. The proposed AGG is compared both conceptually and empirically to previous work, and the impact of data augmentation on the performance of AGG is also briefly discussed. Our experiments reveal that AGG achieved state-of-the-art results in time series data imputation, classification and prediction for the benchmark datasets \emph{Beijing Air Quality}, \emph{PhysioNet Challenge 2012} and \emph{UCI localisation}.
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Submission Number: 933
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