Sustainable Development Goal Relational
Modelling
and Prediction (pp348-367)
Yassir Alharbi, Daniel
Arribas-Bel, and Frans Coenen
doi:
https://doi.org/10.26421/JDI2.3-3
Abstracts:
A methodology for UN Sustainable Development Goal (SDG)
attainment prediction is presented, the Sustainable Development
Goals Correlation Attainment Predictions Extended framework SDG-CAP-EXT.
Unlike previous SDG attainment methodologies, SDG-CAP-EXT takes into
account the potential for a 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. A ensemble approach is presented that
combines the results of a number of alternative causality
relationship identification mechanisms. The identified relationships
are used to build multi-variate time series prediction models that
feed into a bottom-up SDG prediction taxonomy, which is used to make
SDG attainment predictions and rank countries using a proposed
Attainment Likelihood Index that reflects the likelihood of goal
attainment. The framework is fully described and evaluated. The
evaluation demonstrates that the SDG-CAP-EXT framework can produce
better predictions than alternative models that do not consider the
potential for intra-
and inter-causal relationships.
Key words:
Time Series
Correlation and Causality, Hierarchical Classification, Time Series
Prediction and Forecasting, United Nations Sustainable Development
Goals