An interval forecast model for infectious diseases using fuzzy information granulation and spatial-temporal graph neural network

Article type: Research Article

Authors: Hu, Junhua | Zhou, Yingling | Li, Huiyu | Liang, Pei^{; *}

Affiliations: School of Business, Central South University, Changsha, China

Correspondence: [*] Corresponding author. Pei Liang, School of Business, Central South University, Changsha 410083, China. E-mail: shirley_lp@csu.edu.cn.

Abstract: To enhance infection diseases interval prediction, an improved model is proposed by integrating neighborhood fuzzy information granulation (NNIG) and spatial-temporal graph neural network (STGNN). Additionally, the NNIG model can efficiently extract the most representative features from the time series data and identifies the support upper and lower bounds. NNIG model transfers time series data from numerical level to granular level, and processes data feed it into STGNN for interval prediction. Finally, experiments are conducted for evaluation based on the COVID-19 data. The results demonstrate that the NNIG outperforms baseline models. Further, it proves beneficial in offering a valuable approach for policy-making.

Keywords: Time series, fuzzy information granulation, interval prediction, spatial-temporal graph neural network

DOI: 10.3233/JIFS-236766

Journal: Journal of Intelligent & Fuzzy Systems, vol. 47, no. 1-2, pp. 83-97, 2024