Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis

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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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)
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
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