Coenen, Frans 
ORCID: 0000-0003-1026-6649, Alharbi, Yassir ORCID: 0000-0001-6764-030X and Arribas-Bel, Daniel ORCID: 0000-0002-6274-1619
(2020)
Sustainable Development Goal Relational Modelling: Introducing the SDG-CAP Methodology.
In: International Conference on Big Data Analytics and Knowledge Discovery, 2020-9-14 - 2020-9-17, Bratislava, Slovakia.
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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.
| Item Type: | Conference or Workshop Item (Unspecified) |
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| Uncontrolled Keywords: | Time series correlation and causality, Missing values, Hierarchical classification, Time series forecasting, Sustainable Development Goals |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Sep 2020 07:55 |
| Last Modified: | 18 Jan 2023 23:34 |
| DOI: | 10.1007/978-3-030-59065-9_15 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3100520 |
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