A COVID-19 forecasting system using adaptive neuro-fuzzy inference

Kim Tien Ly

Finance Research Letters, 2021, vol. 41, issue C

Abstract: This article proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the number of COVID-19 cases in the United Kingdom. With the combination of artificial neural network and fuzzy logic structure, the model is trained based on collected data. The study examines various factors of ANFIS to come up with an effective time series prediction model. The result indicates that Spain and Italy data can strengthen the predictive power of COVID-19 cases in the UK. It is suggested that the policymakers should adopt Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict contagion effect during the COVID-19 pandemic.

Keywords: ANFIS; Time series; Forecasting system; Coronavirus; Contagion effect (search for similar items in EconPapers)
JEL-codes: C53 C67 G15 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612320316585
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:41:y:2021:i:c:s1544612320316585

DOI: 10.1016/j.frl.2020.101844

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().