Open AccessRobustness Assessment of the RSD t ‐Test for Detecting Trend Turning in a Time Series
Author(s) -
Zuo Bin,
Hou Zhaolu,
Zheng Fei,
Sheng Lifang,
Gao Yang,
Li Jianping
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2019ea001042
Subject(s) - robustness (evolution) , turning point , series (stratigraphy) , computer science , time series , false alarm , structural break , statistics , artificial intelligence , mathematics , machine learning , period (music) , geology , physics , acoustics , paleontology , biochemistry , chemistry , gene
Trend turning (or trend change) is a type of structural change that is common in climate data, and methods for detecting it in time series with multiple turning‐points need to be developed. A recently developed method for this, the running slope difference (RSD) t ‐test, examines trend differences in sub‐series of the sample time series to identify the trend turning‐points. In this paper, we use Monte Carlo simulation to evaluate this method's detection ability. Evaluation results show the method to be an effective tool for detecting trend turning time series and identify three major advantages of the RSD t ‐test: ability to detect multiple turning‐points, capacity to detect all three types of trend turning, and great performance of reducing false alarm rate.