Requirements for Reliable Detection of Early Warning Signals for Critical Transitions in Time Series Data

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals (EWSs) for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this talk, we report our systematic tests of three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen (AUD-JPY) and Swiss Franc-Japanese Yen (CHF-JPY) exchange rates [1]. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals in the three indicators to be concurrent, and to rise to appreciable values. Suspecting that negative results in the literature are the results of low data frequencies, we down sampled the raw data to create time series with time intervals spanning three orders of magnitude, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test.

Authors: 
Siew Ann Cheong, Haoyu Wen and Massimo Pica Ciamarra
Room: 
6
Date: 
Tuesday, September 25, 2018 - 11:15 to 11:30

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