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Prediction of global trade network evolution with uncertain multi-step time series forecasting method

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  • Jinran Chen

    (Capital University of Economics and Business)

Abstract

Predicting the evolutionary trends of complex systems is a critical issue in the field of complex system science. Based on the uncertain theory framework, this study proposes an uncertain multi-step time series forecasting aimed at predicting trends of network evolution, and applies it to the prediction of the future development of the global trade network. Specifically, this study utilizes inter-country input–output tables to construct the global trade network based on social network analysis methods. Furthermore, supply-side and demand-side trade dependency indicators are proposed to identify the evolutionary characteristics of the global trade network. The uncertain multi-step time series forecasting is subsequently utilized to predict network evolution in 2025–2035, and the network associations between China and the five regional trading blocs and countries are mainly analyzed. This study broadens the theoretical approaches for predicting the future evolution of complex systems.

Suggested Citation

  • Jinran Chen, 2024. "Prediction of global trade network evolution with uncertain multi-step time series forecasting method," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 387-414, September.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:3:d:10.1007_s10700-024-09426-w
    DOI: 10.1007/s10700-024-09426-w
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    References listed on IDEAS

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