论文标题
预测图表上的多维过程
Forecasting Multi-Dimensional Processes over Graphs
论文作者
论文摘要
最近在Graph信号处理框架下解决了通过基于图的技术对多变量时间过程的预测。但是，当每个时间序列带有数量的矢量而不是标量量时，表示形式和处理中的问题就会出现。为了解决此问题，我们设计了一个新的框架，并根据图向量自回归模型提出了新的方法。更明确地，我们利用产品图来对高维图数据进行建模，并开发基于多维的矢量自回归模型，以预测未来趋势，这些参数与时间序列和线性计算复杂性无关。数值结果证明了移动点云的预测，证实了我们的发现。
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and propose new methodologies based on the graph vector autoregressive model. More explicitly, we leverage product graphs to model the high-dimensional graph data and develop multi-dimensional graph-based vector autoregressive models to forecast future trends with a number of parameters that is independent of the number of time series and a linear computational complexity. Numerical results demonstrating the prediction of moving point clouds corroborate our findings.
