A new method for detecting abrupt shifts in time series
dc.contributor.author | Boulton, CA | |
dc.contributor.author | Lenton, TM | |
dc.date.accessioned | 2020-01-16T10:38:39Z | |
dc.date.issued | 2019-05-28 | |
dc.description.abstract | Abrupt shifts in time series are a topic of growing interest in a number of research areas. They can be caused by a range of different underlying dynamics, for example, via a mathematical bifurcation, or potentially as the result of an auto-correlated stochastic process (i.e. ‘red’ noise). Here we present a method that detects abrupt shifts by searching for gradient changes that occur over a short space of time. It can be automated, allowing many time series to be analysed by the user at once, such as from high spatial resolution data. Our method detects abrupt shifts regardless of their origin (which it cannot deduce). We present a comparison with the method of abrupt shift detection from the changepoint R package, which is based on changes in mean over the time series. Our method performs better on data with an underlying trend where comparisons of means may fail. | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.identifier.citation | Vol. 8, article 746 | en_GB |
dc.identifier.doi | 10.12688/F1000RESEARCH.19310.1 | |
dc.identifier.grantnumber | NE/P007880/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/40431 | |
dc.language.iso | en | en_GB |
dc.publisher | F1000Research | en_GB |
dc.rights | © 2019 Boulton CA and Lenton TM. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_GB |
dc.subject | Abrupt shifts | en_GB |
dc.subject | Time series analysis | en_GB |
dc.subject | Shift detection | en_GB |
dc.subject | R package | en_GB |
dc.subject | Methods | en_GB |
dc.title | A new method for detecting abrupt shifts in time series | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-01-16T10:38:39Z | |
dc.identifier.issn | 2046-1402 | |
dc.description | This is the published version [version 1; peer review: 2 approved with reservations]. Available on open access from 1000Research via the DOI in this record. | en_GB |
dc.identifier.journal | F1000Research | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019 | |
exeter.funder | ::Natural Environment Research Council (NERC) | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-05-28 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-01-15T11:40:55Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-01-16T10:38:55Z | |
refterms.panel | C | en_GB |
refterms.depositException | publishedGoldOA |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © 2019 Boulton CA and Lenton TM. This is an open access article distributed under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.