|Authors||J. Krishnan, R. Money, B. Beferull-Lozano and E. Isufi|
|Title||Simplicial Vector Autoregressive Model for Streaming Edge Flows|
|Project(s)||Signal and Information Processing for Intelligent Systems|
|Publication Type||Proceedings, refereed|
|Year of Publication||2023|
|Conference Name||2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)|
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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