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A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series

Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametri...

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Main Authors: Ali, Norhaslinda, Abdul Ghani, Nur Adilah
Format: Article
Published: Universiti Malaysia Perlis 2024
Online Access:http://psasir.upm.edu.my/id/eprint/106398/
https://ejournal.unimap.edu.my/index.php/amci/article/view/245
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spelling my.upm.eprints.1063982024-09-26T07:48:21Z http://psasir.upm.edu.my/id/eprint/106398/ A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series Ali, Norhaslinda Abdul Ghani, Nur Adilah Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sens Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sens Innovative Trend Analysis (MMKITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMKITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power. Universiti Malaysia Perlis 2024-02-14 Article PeerReviewed Ali, Norhaslinda and Abdul Ghani, Nur Adilah (2024) A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series. Applied Mathematics and Computational Intelligence, 13 (1). pp. 36-51. ISSN 2289-1315; ESSN: 2289-1323 https://ejournal.unimap.edu.my/index.php/amci/article/view/245 10.58915/amci.v13iNo.1.245
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sens Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sens Innovative Trend Analysis (MMKITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMKITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power.
format Article
author Ali, Norhaslinda
Abdul Ghani, Nur Adilah
spellingShingle Ali, Norhaslinda
Abdul Ghani, Nur Adilah
A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
author_facet Ali, Norhaslinda
Abdul Ghani, Nur Adilah
author_sort Ali, Norhaslinda
title A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
title_short A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
title_full A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
title_fullStr A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
title_full_unstemmed A comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
title_sort comparison of the rank-based and slope-based nonparametric tests for trend detection in climate time series
publisher Universiti Malaysia Perlis
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/106398/
https://ejournal.unimap.edu.my/index.php/amci/article/view/245
_version_ 1811685934127644672
score 13.211869

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