AI Article Synopsis

  • Estimating dependencies in multidimensional time series data is complicated by large streaming datasets and asynchronous collection from various sources.
  • To address this, the authors propose a Leveraging Score Sampling (LSS) method for real-time inference in streaming vector autoregressive (VAR) models, ensuring efficient parameter estimation with statistical reliability.
  • The LSS method allows for independent updates across different data dimensions, making it suitable for decentralized environments like sensor networks, and its effectiveness is demonstrated through various datasets including synthetic, gas sensor, and seismic data.

Article Abstract

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 (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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556430PMC

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