@inproceedings {42847,
	title = {Simplicial Vector Autoregressive Model for Streaming Edge Flows},
	journal = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)},
	year = {2023},
	note = {Matlab Codes:\&nbsp;https://github.com/Joshinpk/Simplicial_VAR_Model},
	month = {05/2023},
	publisher = {IEEE},
	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.},
	isbn = {978-1-7281-6328-4},
	doi = {10.1109/ICASSP49357.2023.10096095},
	url = {https://ieeexplore.ieee.org/document/10096095},
	author = {Krishnan, Joshin and Money, Rohan and Beferull-Lozano, Baltasar and Isufi, Elvin}
}
