Person:
Moradi, Mohammad Mehdi

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49607

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Moradi

First Name

Mohammad Mehdi

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ORCID

0000-0003-3905-4498

person.page.upna

811779

Name

Filters

20211
Reset filters

Settings

Author: Moradi, Mohammad Mehdi × Subject: Adaptive sliding window ×

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    PublicationOpen Access
    Locally adaptive change-point detection (LACPD) with applications to environmental changes
    (Springer, 2021) Moradi, Mohammad Mehdi; Montesino San Martín, Manuel; Ugarte Martínez, María Dolores; Militino, Ana F.; Estatistika, Informatika eta Matematika; Institute for Advanced Materials and Mathematics - INAMAT2; Estadística, Informática y Matemáticas
    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.