On the use of evolutionary time series analysis for segmenting paleoclimate data

Abstract

Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.

A mixed distribution to fix the threshold for Peak-Over-Threshold wave height estimation

“…They are present in different fields of application, e.g. climate 7 , oceanography 8 , biology 9 and much more. In addition, they are used for different research objectives, such as classification 10 , tipping point detection 11 , forecasting 12 , etc.…”

Representation of European hydroclimatic patterns with self-organizing maps

“…In addition, they were also used for interpolation of palaeovegetation data at a global scale (Grieger, 2002), as well as in multi-proxy reconstructions (Guiot et al, 2005). Artificial neural networks still remain popular today (Carro-Calvo et al, 2013; Pérez-Ortiz et al, 2019), while other promising approaches, such as boosted regression trees, are also utilized (Juggins et al, 2015; Salonen et al, 2014). We can see that the main application of machine learning in palaeoclimatology is for transforming the climate signal encapsulated to proxy time series to climate variables.…”