Deconvolution of mixing time series on a graph
Deconvolution of mixing time series on a graph
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发表时间：
2011-05
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通讯作者：
A. Blocker;E. Airoldi
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作者：
A. Blocker;E. Airoldi
在许多应用中，我们有兴趣从间接测量中对潜在的时间序列进行推断，这些间接测量通常是由混合或聚合产生的低维投影。正电子发射断层扫描、超分辨率和网络流量监控就是一些例子。在这种情况下的推理需要解决一系列不适定的逆问题yt = Axt，其中投影机制提供了关于A的信息。我们考虑的问题，其中A指定的时间序列是突发和稀疏的图上的混合。我们开发了一个多级状态空间模型的混合时间序列和一个有效的方法来推断。一个简单的模型是用来校准正则化参数，导致有效的推断，在多级状态空间模型。我们应用这种方法来估计点到点的网络上的流量从聚合测量的问题。我们的解决方案优于现有的方法，这个问题，我们的两个阶段的方法提出了一个有效的多变量时间序列的多级模型的推理策略。
In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Positron emission tomography, super-resolution, and network traffic monitoring are some examples. Inference in such settings requires solving a sequence of ill-posed inverse problems, yt = Axt , where the projection mechanism provides information on A. We consider problems in which A specifies mixing on a graph of times series that are bursty and sparse. We develop a multilevel state-space model for mixing times series and an efficient approach to inference. A simple model is used to calibrate regularization parameters that lead to efficient inference in the multilevel state-space model. We apply this method to the problem of estimating point-to-point traffic flows on a network from aggregate measurements. Our solution outperforms existing methods for this problem, and our two-stage approach suggests an efficient inference strategy for multilevel models of multivariate time series.