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.

翻译：将时变图信号建模为平稳的时间-顶点随机过程，能够通过高效利用该过程在不同图节点和时间点上的相关模式来推断缺失信号值。本研究提出了一种计算图自回归滑动平均（graph ARMA）过程的算法，该算法从信号的不完全实现中学习联合时间-顶点功率谱密度，以完成信号插值任务。我们的解决方案首先从部分观测的实现中粗略估计过程的联合频谱，随后通过凸松弛将其投影到图ARMA过程的谱流形上以细化该估计。最后，基于学习到的模型估计初始缺失的信号值。实验结果表明，所提方法在时间-顶点信号估计问题中实现了高精度。