Every week, we select a recently published Open Access article to feature. This week’s article is from Environmetrics and proposes a novel changepoint detection algorithim  without obscurity caused by down-scaling processes. 

The article’s abstract is given below, with the full article available to read here. 

Lowther, A. P., Killick, R., & Eckley, I. A. (2022). Detecting changes in mixed-sampling rate data sequences. Environmetrics, e2762. https://doi.org/10.1002/env.2762

Different environmental variables are often monitored using different sampling rates; examples include half-hourly weather station measurements, daily CO2 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.