摘要翻译：
平稳性是许多随机过程统计模型中的一个关键假设。随着图信号处理领域的发展，传统的广义平稳性概念已经扩展到定义在图顶点上的随机过程。众所周知的谱图核方法都假定图上的随机过程是平稳的。虽然机器学习和信号处理文献中已经提出了许多方法来建模图上的平稳随机过程，但由于大多数数据是非平稳过程，这些方法对描述真实世界的数据集具有太大的限制。为了刻画图上的非平稳过程，我们提出了一个新的模型和一个计算效率高的算法，该算法将一个大图划分成不相交的簇，使得该过程在每个簇上是平稳的，但在簇间是独立的。我们在达拉斯-沃思堡地区的一个细粒度高速公路旅行时间的大规模数据集上评估了我们的交通预测模型。我们的方法的精度非常接近最先进的基于图的深度学习方法，而我们的模型的计算复杂度大大降低。
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英文标题：
《Piecewise Stationary Modeling of Random Processes Over Graphs With an
  Application to Traffic Prediction》
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作者：
Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R. Narayanan
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最新提交年份：
2019
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Stationarity is a key assumption in many statistical models for random processes. With recent developments in the field of graph signal processing, the conventional notion of wide-sense stationarity has been extended to random processes defined on the vertices of graphs. It has been shown that well-known spectral graph kernel methods assume that the underlying random process over a graph is stationary. While many approaches have been proposed, both in machine learning and signal processing literature, to model stationary random processes over graphs, they are too restrictive to characterize real-world datasets as most of them are non-stationary processes. In this paper, to well-characterize a non-stationary process over graph, we propose a novel model and a computationally efficient algorithm that partitions a large graph into disjoint clusters such that the process is stationary on each of the clusters but independent across clusters. We evaluate our model for traffic prediction on a large-scale dataset of fine-grained highway travel times in the Dallas--Fort Worth area. The accuracy of our method is very close to the state-of-the-art graph based deep learning methods while the computational complexity of our model is substantially smaller.
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PDF链接：
https://arxiv.org/pdf/1711.06954