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      Permanent link:
      https://www.ias.ac.in/article/fulltext/conf/001/01/0051-0060

    • Keywords

       

      Interdependence; correlation; inner composition alignment; time series analysis

    • 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.

      PACS Nos 05.45.; 02.50.-r; 02.50.Tt; 05.45.Tp; 02.70.Rr; 93.30.Bz; 93.30.Db; 92.60.Ry; 92.70.Kb; 92.70.Mn; 89.65.Gh; 89.75.Fb

    • Author Affiliations

       

      BEDARTHA GOSWAMI1 1 PAUL SCHULTZ1 1 BIRTE HEINZE2 NORBERT MARWAN2 BENJAMIN BODIRSKY2 HERMANN LOTZE-CAMPEN1 JÜRGEN KURTHS1

      1. Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam, Germany
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