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BayesProject: Fast computation of a projection direction for multivariate changepoint detection

机译：BayesProject：用于多变量转换点检测的投影方向的快速计算

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摘要

This article focuses on the challenging problem of efficiently detecting changes in mean within multivariate data sequences. Multivariate changepoints can be detected by projecting a multivariate series to a univariate one using a suitable projection direction that preserves a maximal proportion of signal information. However, for some existing approaches the computation of such a projection direction can scale unfavourably with the number of series and might rely on additional assumptions on the data sequences, thus limiting their generality. We introduce BayesProject, a computationally inexpensive Bayesian approach to compute a projection direction in such a setting. The proposed approach allows the incorporation of prior knowledge of the changepoint scenario, when such information is available, which can help to increase the accuracy of the method. A simulation study shows that BayesProject is robust, yields projections close to the oracle projection direction and, moreover, that its accuracy in detecting changepoints is comparable to, or better than, existing algorithms while scaling linearly with the number of series.
机译：本文侧重于有效地检测多元数据序列内平均值的挑战性问题。可以通过使用合适的投影方向将多变量序列突出到单变频器，以保留最大比例的信号信息来检测多变量序列来检测多变量转换点。然而，对于一些现有的方法，这种投影方向的计算可以与串的数量不利地规模，并且可以依赖于数据序列上的额外假设，从而限制了它们的一般性。我们介绍了BayesProject，一种计算地廉价的贝叶斯方法，可以在这种设置中计算投影方向。当此类信息可用时，所提出的方法允许纳入更改点情景的先验知识，这有助于提高方法的准确性。仿真研究表明，BayesProject是稳健的，产生接近Oracle投影方向的投影，而且，其在检测转换点的准确性比现有算法相当，而在线性地与串联进行线性缩放。

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