Publication: Locally adaptive change-point detection (LACPD) with applications to environmental changes
| dc.contributor.author | Moradi, Mohammad Mehdi | |
| dc.contributor.author | Montesino San Martín, Manuel | |
| dc.contributor.author | Ugarte Martínez, María Dolores | |
| dc.contributor.author | Militino, Ana F. | |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.date.accessioned | 2021-12-28T10:45:01Z | |
| dc.date.available | 2021-12-28T10:45:01Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | We propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set of time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate for the lack of enough data near the time series’ tails. Through a simulation study, we analyse its behaviour in the presence of an early/middle/late change-point in the mean, and compare its performance with some of the frequently used and recently developed change-point detection methods in terms of power, type I error probability, area under the ROC curves (AUC), absolute bias, variance, and root-mean-square error (RMSE). We conclude that LACPD outperforms other methods by maintaining a low type I error probability. Unlike some other methods, the performance of LACPD does not depend on the time index of change-points, and it generally has lower bias than other alternative methods. Moreover, in terms of variance and RMSE, it outperforms other methods when change-points are close to the time series’ tails, whereas it shows a similar (sometimes slightly poorer) performance as other methods when change-points are close to the middle of time series. Finally, we apply our proposal to two sets of real data: the well-known example of annual flow of the Nile river in Awsan, Egypt, from 1871 to 1970, and a novel remote sensing data application consisting of a 34-year time-series of satellite images of the Normalised Difference Vegetation Index in Wadi As-Sirham valley, Saudi Arabia, from 1986 to 2019. We conclude that LACPD shows a good performance in detecting the presence of a change as well as the time and magnitude of change in real conditions. | en |
| dc.description.sponsorship | This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE), Project PID2020-113125RB-I00 (AEI) and the Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under Agreement LCF/PR/PR15/51100007. | en |
| dc.format.extent | 19 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1007/s00477-021-02083-0 | |
| dc.identifier.issn | 1436-3240 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41493 | |
| dc.language.iso | eng | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Stochastic Environmental Research and Risk Assessment, 2021 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/ | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/ | en |
| dc.relation.publisherversion | http://doi.org/10.1007/s00477-021-02083-0 | |
| dc.rights | © The Author(s) 2021. Creative Commons Attribution 4.0 International License | es_ES |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Adaptive sliding window | en |
| dc.subject | Normalised difference vegetation index | en |
| dc.subject | Satellite images | en |
| dc.subject | Sub-sampling | en |
| dc.title | Locally adaptive change-point detection (LACPD) with applications to environmental changes | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dspace.entity.type | Publication | |
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