Different environmental variables are often monitored using different sampling rates; examples include half-hourly weather station measurements, daily ${\text{CO}}_2$ data, and six-day satellite data. Further when researchers want to combine the data into a single analysis this often requires data aggregation or down-scaling. When one is seeking to identify changes within multivariate data, the aggregation and/or down-scaling processes obscure the changes we seek. In this article, we propose a novel changepoint detection algorithm which can analyze multiple time series for co-occurring changepoints with potentially different sampling rates, without requiring preprocessing to a standard sampling scale. We demonstrate the algorithm on synthetic data before providing an example identifying simultaneous changes in multiple variables at a location on the Greenland ice sheet using synthetic aperture radar and weather station data.

中文翻译：

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检测混合采样率数据序列的变化

不同的环境变量通常使用不同的采样率进行监测；例子包括半小时气象站测量，每天${\text{一氧化碳}}_{\mathrm{2个}}$数据和六天的卫星数据。此外，当研究人员想要将数据组合成单一分析时，这通常需要数据聚合或缩小尺度。当人们试图识别多变量数据中的变化时，聚合和/或缩减过程会掩盖我们寻求的变化。在本文中，我们提出了一种新颖的变点检测算法，该算法可以分析多个时间序列以寻找具有潜在不同采样率的同时发生的变点，而无需预处理到标准采样比例。我们在使用合成孔径雷达和气象站数据提供示例识别格陵兰冰盖上某个位置的多个变量的同时变化之前，演示了合成数据的算法。