Detecting changes in mixed-sampling rate data sequences
Different environmental variables are often monitored using different sampling rates; examples include half-hourly weather station measurements, daily (Formula presented.) data, and six-day satellite data. Further when researchers want to combine the data into a single analysis this often requires d...
| Published in: | Environmetrics |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://eprints.lancs.ac.uk/id/eprint/175217/ https://eprints.lancs.ac.uk/id/eprint/175217/1/draft_ol_black.pdf |
| Summary: | Different environmental variables are often monitored using different sampling rates; examples include half-hourly weather station measurements, daily (Formula presented.) 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. |
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