Abstract
A mechanism for predicting whether individual regions will meet there UN Sustainability for Development Goals (SDGs) is presented which takes into consideration the potential relationships between time series associated with individual SDGs, unlike previous work where an independence assumption was made. The challenge is in identifying the existence of relationships and then using these relationships to make SDG attainment predictions. To this end the SDG Correlation/Causal Attainment Prediction (SDG-CAP) methodology is presented. Five alternative mechanisms for determining time series relationships are considered together with three prediction mechanisms. The results demonstrate that by considering the relationships between time series, by combining a number of popular causal and correlation identification mechanisms, more accurate SDG forecast predictions can be made.
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Alharbi, Y., Coenen, F., Arribas-Bel, D. (2020). Sustainable Development Goal Relational Modelling: Introducing the SDG-CAP Methodology. In: Song, M., Song, IY., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science(), vol 12393. Springer, Cham. https://doi.org/10.1007/978-3-030-59065-9_15
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