Sustainable Development Goal Attainment Prediction: A Hierarchical Framework using Time Series Modelling

Alharbi, Yassir ORCID: 0000-0001-6764-030X, Arribas-Be, Daniel and Coenen, Frans ORCID: 0000-0003-1026-6649 (2019) Sustainable Development Goal Attainment Prediction: A Hierarchical Framework using Time Series Modelling. In: 11th International Conference on Knowledge Discovery and Information Retrieval, 2019-9-17 - 2019-9-19.

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Official URL: http://dx.doi.org/10.5220/0008067202970304

Abstract

A framework is presented which can be used to forecast weather an individual geographic area will meet its UN Sustainable Development Goals, or not, at some time t. The framework comprises a bottom up hierarchical classification system where the leaf nodes hold forecast models and the intermediate nodes and root node “logical and” operators. Features of the framework include the automated generation of the: associated taxonomy, the threshold values with which leaf node prediction values will be compared and the individual forecast models. The evaluation demonstrates that the proposed framework can be successfully employed to predict whether individual geographic areas will meet their SDGs.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Bottom-up Hierarchical Classification, Time Series Forecasting, UN Sustainable Development Goals
Depositing User:	Symplectic Admin
Date Deposited:	10 Sep 2019 09:53
Last Modified:	19 Jan 2023 00:27
DOI:	10.5220/0008067202970304
Open Access URL:	https://www.insticc.org/Primoris/Resources/PaperPd...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3054104