Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions
Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions
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多个非平稳时间序列的探索性建模：潜在过程结构和分解
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
M. West
中科院分区：
文献类型：
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作者：
R. Prado;M. West
我们描述和说明了贝叶斯方法来建模和分析多个非平稳时间序列。首先是假设由潜在但不可观测的过程驱动的相关时间序列集合的单变量模型，称为动态潜在因素过程。我们将重点放在这样的模型上，在这些模型中，因子过程以及观察到的时间序列都是由能够灵活地表示观察到的非平稳特征范围的时变自回归来建模的。我们强调了时间序列分解的概念和新方法，以推断时间序列中潜在成分的特征，并将单变量分解分析与潜在的多变量动态因素结构联系起来。我们的激励应用是对杜克大学正在进行的一项脑电研究中的多个脑电痕迹的分析。在这项研究中，接受ECT治疗的个体在不同的头皮位置产生了多个EEG痕迹，生理兴趣在于识别系列之间的依赖和不同之处。除了序列的多变量和非平稳方面，这一领域还提供了关于将时间序列分解成潜在的、物理上可解释的分量的新结果的说明；这在对一个脑电数据集的数据分析中得到了说明。文中还讨论了当前和未来的研究方向。
We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with univariate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate univariate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions.