Overview of the latent graph structure learning (L-GSL). (a) Key nodes chosen at random (depicted as gray circles) are used to measure the significance of a query node (s...
Abstract:
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencie...Show MoreMetadata
Abstract:
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect. To confront these complexities, we introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23% reduction in mean squared error (MSE) compared to existing models.
Overview of the latent graph structure learning (L-GSL). (a) Key nodes chosen at random (depicted as gray circles) are used to measure the significance of a query node (s...
Published in: IEEE Access ( Volume: 11)
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