Evolution Backcasting of Edge Flows From Partial Observations Using Simplicial Vector Autoregressive Models

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)

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This paper proposes a novel algorithm to retroactively compute the evolution of edge signals from a given sequence of partial observations from topological structures, a concept referred to as evolution backcasting. Our backcasting algorithm exploits the spatio-temporal dependencies present in the real-world edge signals using the simplicial vector autoregressive (S-VAR) model. The proposed algorithm jointly estimates the S-VAR filter coefficients and recovers missing data from the partial observations. Subsequently, the algorithm capitalizes on the learned S-VAR model and the reconstructed signals to execute the backcasting of edge signal evolution. Using traffic and water distribution networks as case studies, we showcase the superior capabilities of our algorithm compared with baseline alternatives.
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Key words
Simplicial convolution,simplicial vector autoregressive model,Hodge Laplacians
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