AuthorsJ. Krishnan, R. Money, B. Beferull-Lozano and E. Isufi
TitleSimplicial Vector Autoregressive Model for Streaming Edge Flows
AfilliationMachine Learning
Project(s)Signal and Information Processing for Intelligent Systems
Publication TypeProceedings, refereed
Year of Publication2023
Conference Name2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
Date Published05/2023
ISBN Number978-1-7281-6328-4

Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network structures such as edges, triangles, etc. The proposed model shares parameters across the simplicial signals by leveraging the simplicial convolutional filter and captures structure-aware spatio-temporal dependencies of the time-varying processes. Targetting the streaming signals from the real-world nonstationary networks, we develop a group-lasso-based online strategy to learn the proposed model. Using traffic and water distribution networks, we demonstrate that the proposed model achieves competitive signal prediction accuracy with a significantly less number of parameters than the VAR models.

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Citation Key42847