Authors | J. Krishnan, R. Money, B. Beferull-Lozano and E. Isufi |
Title | Simplicial Vector Autoregressive Model for Streaming Edge Flows |
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Status | Published |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023) |
Date Published | 05/2023 |
Publisher | IEEE |
ISBN Number | 978-1-7281-6328-4 |
Abstract | 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. ICASSP 2023 has identified this paper as part of the exclusive group, ranking it within the top 3% of all accepted papers. |
Notes | Matlab Codes: https://github.com/Joshinpk/Simplicial_VAR_Model |
URL | https://ieeexplore.ieee.org/document/10096095 |
DOI | 10.1109/ICASSP49357.2023.10096095 |
Citation Key | 42847 |