Authors:
Yassir Alharbi
1
;
2
;
Daniel Arribas-Bel
3
and
Frans Coenen
2
Affiliations:
1
Almahd College, Taibah University, Al-Madinah Al-Munawarah, Saudi Arabia
;
2
Department of Computer Science, The University of Liverpool, Liverpool L69 3BX, U.K.
;
3
Department of Geography and Planning, The University of Liverpool, Liverpool L69 3BX, U.K.
Keyword(s):
Time Series Causality, Missing Values, Hierarchical Classification, Time Series Forecasting, Sustainable Development Goals.
Abstract:
A framework for UN Sustainability for Development Goal (SDG) attainment prediction is presented, the SDG Track, Trace & Forecast (SDG-TTF) framework. Unlike previous SDG attainment frameworks, SDG-TTF takes into account the potential for causal relationship between SDG indicators both with respect to the geographic entity under consideration (intra-entity), and neighbouring geographic entities to the current entity (inter-entity). The challenge is in the discovery of such causal relationships. Six alternatives mechanisms are considered. The identified relationships are used to build multivariate time series prediction models which feed into a bottom-up SDG prediction taxonomy, which in turn is used to make SDG attainment predictions. The framework is fully described and evaluated. The evaluation demonstrates that the SDG-TTF framework is able to produce better predictions than alternative models which do not take into consideration the potential for intra and inter-causal relationshi
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