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Asynchronous graph generator

delete2025-07-07
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OA
AI
C
Christopher P. Ley
F
Felipe Tobar
DOI：10.1016/j.sigpro.2025.110183delete
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Abstract

Abstract

En 中文
• A novel interpretation of sparse multi-channel time series as an asynchronous graph, where nodes represent the observations. • Novel use of embeddings to encode temporal and channel features which can be leveraged through graph attention to model the relationships among observations. • The introduction of conditional attention generation, a mechanism to generate new observations conditioned on given temporal/channel features. • An experimental validation of the AGG (Asynchronous Graph Generator) on standard benchmarks against the state of the art. • A study of the limiting performance of the AGG from the perspective of data augmentation.

Journal

Signal Processing cover
Signal Processing
IF:
3.6
Papers:
9.9K
Citations:
1.7W

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