Robustness Assessment of the RSD t‐Test for Detecting Trend Turning in a Time Series

• Main Authors: Bin Zuo, Zhaolu Hou, Fei Zheng, Lifang Sheng, Yang Gao, Jianping Li
• Format: Article
• Language: English
• Published: American Geophysical Union (AGU) 2020-05-01
• Series: Earth and Space Science
• Subjects: trend turning; trend change point; climate change; time series analysis; Monte Carlo simulation
• Online Access: https://doi.org/10.1029/2019EA001042

Abstract 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.