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  Inferring interdependencies from short time series

Goswami, B., Schultz, P., Heinze, B., Marwan, N., Bodirsky, B. L., Lotze-Campen, H., Kurths, J. (2017): Inferring interdependencies from short time series. - Indian Academy of Sciences Conference Series, 1, 1, 51-60.
https://doi.org/10.29195/iascs.01.01.0021

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Goswami, Bedartha1, Author              
Schultz, Paul1, Author              
Heinze, Birte1, Author              
Marwan, Norbert1, Author              
Bodirsky, Benjamin Leon1, Author              
Lotze-Campen, Hermann1, Author              
Kurths, Jürgen1, Author              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the interdependencies between pairs of datasets. However, most of the classic and state-of-the-art interdependence estimation techniques require sufficiently long time series for their successful application. In this study, we present a modification of the inner composition alignment approach (IOTA), correspondingly termed mIOTA, and review its advantages. Using two coupled auto-regressive stochastic processes, we demonstrate the discriminating power of mIOTA and show that it outperforms standard interdependence measures. We then use mIOTA to derive econo-climatic networks of interdependencies between economic indicators and climatic variability for Sub-Saharan Africa (AFR) and South Asia including India (SAS). Our analysis uncovers that crop production in AFR is strongly interdependent with the regional rainfall. While the gross domestic product (GDP) as an economic indicator in AFR is independent of climatic factors, we find that precipitation in the SAS influences the regional GDP, likely reflecting the influence of the summer monsoons. The differences in the interdependence structures between AFR and SAS reflect an underlying structural difference in their overall economies, as well as their agricultural sectors.

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 Dates: 2017
 Publication Status: Finally published
 Pages: -
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 Identifiers: DOI: 10.29195/iascs.01.01.0021
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
PIKDOMAIN: Climate Impacts & Vulnerabilities - Research Domain II
eDoc: 7705
Working Group: Land Use and Resilience
Working Group: Development of advanced time series analysis techniques
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Title: Indian Academy of Sciences Conference Series
Source Genre: Journal, other
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Pages: - Volume / Issue: 1 (1) Sequence Number: - Start / End Page: 51 - 60 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/indian-academy-of-sciences-conference-series