Asynchronous Graph Generator

• URL: http://arxiv.org/abs/2309.17335v4
• Date: Wed, 16 Apr 2025 21:17:25 GMT
• Title: Asynchronous Graph Generator
• Abstract summary: AGG encodes measurements, timestamps and channel-specific features directly in the nodes via learnable embeddings.<n>AGG performs imputation by emphconditional attention generation, i.e., by creating a new node conditioned on given timestamps and channel specification.<n>Our experiments reveal that AGG achieved state-of-the-art results in time series imputation, classification and prediction for the benchmark datasets emphBeijing Air Quality, emphPhysioNet ICU 2012 and emphUCI localisation, outperforming other recent attention
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the asynchronous graph generator (AGG), a novel graph attention network for imputation and prediction of multi-channel time series. Free from recurrent components or assumptions about temporal/spatial regularity, AGG encodes measurements, timestamps and channel-specific features directly in the nodes via learnable embeddings. Through an attention mechanism, these embeddings allow for discovering expressive relationships among the variables of interest in the form of a homogeneous graph. Once trained, AGG performs imputation by \emph{conditional attention generation}, i.e., by creating a new node conditioned on given timestamps and channel specification. The proposed AGG is compared to related methods in the literature and its performance is analysed from a data augmentation perspective. Our experiments reveal that AGG achieved state-of-the-art results in time series imputation, classification and prediction for the benchmark datasets \emph{Beijing Air Quality}, \emph{PhysioNet ICU 2012} and \emph{UCI localisation}, outperforming other recent attention-based networks.

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