Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series
Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series
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发表时间：
2019-04
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通讯作者：
Rui Xie;Zengyan Wang;Shuyang Bai;Ping Ma;Wenxuan Zhong
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作者：
Rui Xie;Zengyan Wang;Shuyang Bai;Ping Ma;Wenxuan Zhong
实时估计多维时间序列数据的依赖结构具有挑战性。对于大量的流数据，当多维数据在分布式节点上异步收集时，问题变得更加困难，这促使我们从流中采样代表性的数据点。我们提出了一个杠杆分数抽样（LSS）的方法，有效的在线推理的流向量自回归（VAR）模型。我们定义了流VAR模型的杠杆分数，以便LSS方法实时选择信息丰富的数据点，并具有参数估计效率的统计保证。此外，我们的LSS方法可以直接部署在异步分散环境中，例如，一个传感器网络没有融合中心，并产生异步共识在线参数估计随着时间的推移。通过利用VAR模型的时间依赖性结构，LSS方法在每个维度上独立地选择样本，从而能够异步地更新估计。我们说明了LSS方法在合成，气体传感器和地震数据集的有效性。
Estimating the dependence structure of multidimensional time series data in real-time is challenging. With large volumes of streaming data, the problem becomes more difficult when the multidimensional data are collected asynchronously across distributed nodes, which motivates us to sample representative data points from streams. We propose a leverage score sampling (LSS) method for efficient online inference of the streaming vector autoregressive (VAR) model. We define the leverage score for the streaming VAR model so that the LSS method selects informative data points in real-time with statistical guarantees of parameter estimation efficiency. Moreover, our LSS method can be directly deployed in an asynchronous decentralized environment, e.g., a sensor network without a fusion center, and produce asynchronous consensus online parameter estimation over time. By exploiting the temporal dependence structure of the VAR model, the LSS method selects samples independently on each dimension and thus is able to update the estimation asynchronously. We illustrate the effectiveness of the LSS method in synthetic, gas sensor and seismic datasets.