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Inferring interdependencies from short time series

Urheber*innen
/persons/resource/goswami

Goswami,  Bedartha
Potsdam Institute for Climate Impact Research;

/persons/resource/Paul.Schultz

Schultz,  Paul
Potsdam Institute for Climate Impact Research;

/persons/resource/birte.heinze

Heinze,  Birte
Potsdam Institute for Climate Impact Research;

/persons/resource/Marwan

Marwan,  Norbert
Potsdam Institute for Climate Impact Research;

/persons/resource/Bodirsky

Bodirsky,  Benjamin Leon
Potsdam Institute for Climate Impact Research;

/persons/resource/Lotze-Campen

Lotze-Campen,  Hermann
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_21797
Zusammenfassung
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.