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Detecting changes in mixed‐sampling rate data sequences

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  • Aaron Paul Lowther
  • Rebecca Killick
  • Idris Arthur Eckley

Abstract

Different environmental variables are often monitored using different sampling rates; examples include half‐hourly weather station measurements, daily CO2$$ {\mathrm{CO}}_2 $$ 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.

Suggested Citation

  • Aaron Paul Lowther & Rebecca Killick & Idris Arthur Eckley, 2023. "Detecting changes in mixed‐sampling rate data sequences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
  • Handle: RePEc:wly:envmet:v:34:y:2023:i:1:n:e2762
    DOI: 10.1002/env.2762
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    References listed on IDEAS

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    1. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
    2. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
    3. Tengyao Wang & Richard J. Samworth, 2018. "High dimensional change point estimation via sparse projection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 57-83, January.
    4. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
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