Alharbi, Yassir ORCID: 0000-0001-6764-030X, Arribas-Bel, Daniel
ORCID: 0000-0002-6274-1619 and Coenen, Frans
ORCID: 0000-0003-1026-6649
(2021)
Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis.
In: 2nd International Conference on Deep Learning Theory and Applications, 2021-7-7 - 2021-7-9.
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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 relationships.
Item Type: | Conference or Workshop Item (Unspecified) |
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Uncontrolled Keywords: | Time Series Causality, Missing Values, Hierarchical Classification, Time Series Forecasting, Sustainable Development Goals |
Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science and Engineering > School of Environmental Sciences |
Depositing User: | Symplectic Admin |
Date Deposited: | 12 Oct 2021 10:46 |
Last Modified: | 18 Jan 2023 21:27 |
DOI: | 10.5220/0010546101230131 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3140166 |