Learning Graph ARMA Processes From Time-Vertex Spectra

Date

2024-01-01

Author

Güneyi, Eylem Tuǧçe
Yaldiz, Berkay
Canbolat, Abdullah
Vural, Elif

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. Our solution relies on first roughly estimating the joint spectrum of the process from partially observed realizations and then refining this estimate by projecting it onto the spectrum manifold of the graph ARMA process through convex relaxations. The initially missing signal values are then estimated based on the learnt model. Experimental results show that the proposed approach achieves high accuracy in time-vertex signal estimation problems.

Subject Keywords

graph ARMA models, Graph processes, joint power spectral density, time-varying graph signals, time-vertex processes

URI

https://hdl.handle.net/11511/108181

Collections

Department of Electrical and Electronics Engineering, Article