Sustainable Development Goals Monitoring and Forecasting using Time Series Analysis

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, SDGTTF 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 intercausal relationships.