Gaussian Processes for time-marked time-series data
Gaussian Processes for time-marked time-series data
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发表时间：
2012-03
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通讯作者：
J. Cunningham;Zoubin Ghahramani;C. Rasmussen
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作者：
J. Cunningham;Zoubin Ghahramani;C. Rasmussen
在许多情况下，数据被收集为多个时间序列，其中每个记录的时间序列是对一些感兴趣的潜在动态过程的观察。这些观测结果通常带有已知事件时间的时间标记，人们希望进行一系列标准分析。当只有一个时间标记时，人们只需在该标记上按时间排列观察。当存在多个时间标记并且在不同的时间序列观测中处于不同的时间时，这些分析更加复杂。我们描述了一个高斯过程模型，用于分析具有多个时间标记的多个时间序列，并在各种数据上对其进行了测试。
In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at dierent times on dierent time series observations, these analyses are more dicult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.