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A wavelet-based approach for imputation in nonstationary multivariate time series

机译：基于小波的非间断多变量时间序列的估算方法

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摘要

Many multivariate time series observed in practice are second order nonstationary, i.e. their covariance properties vary over time. In addition, missing observations in such data are encountered in many applications of interest, due to recording failures or sensor dropout, hindering successful analysis. This article introduces a novel method for data imputation in multivariate nonstationary time series, based on the so-called locally stationary wavelet modelling paradigm. Our methodology is shown to perform well across a range of simulation scenarios, with a variety of missingness structures, as well as being competitive in the stationary time series setting. We also demonstrate our technique on data arising in a health monitoring application.
机译：在实践中观察到的许多多变量时间序列是二阶非视野，即它们的协方差特性随​​着时间的变化而变化。此外，由于录制故障或传感器丢失，妨碍了成功分析，在许多感兴趣的应用中遇到了在这种数据的许多应用中遇到缺失的观察。本文基于所谓的本地固定小波建模范式，介绍了多变量非间断时间序列中数据载荷的新方法。我们的方法显示在一系列仿真方案中，具有各种缺失结构，以及在静止时间序列设置中具有竞争力。我们还展示了我们对健康监测申请中产生的数据的技术。

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