China's Economic Forecast Based on Machine Learning and Quantitative Easing.
Journal
Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357
Informations de publication
Date de publication:
2022
2022
Historique:
received:
11
02
2022
accepted:
03
03
2022
entrez:
5
4
2022
pubmed:
6
4
2022
medline:
7
4
2022
Statut:
epublish
Résumé
In this paper, six variables, including export value, real exchange rate, Chinese GDP, and US IPI, and their seasonal variables, are used as determinants to model and forecast China's export value to the US using three methods: BP neural network, ARIMA, and AR-GARCH. Error indicators were chosen to compare the simulated and predicted results of the three models with the real values. It is found that the results of all three models are satisfactory, although there are some differences in their simulation and forecasting capabilities, but the ARIMA model has a clear advantage. This paper analyses the reasons for these results and proposes suggestions for improving China's exports in the context of the models.
Identifiants
pubmed: 35378809
doi: 10.1155/2022/2404174
pmc: PMC8976612
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2404174Informations de copyright
Copyright © 2022 Chang Qiu.
Déclaration de conflit d'intérêts
The author declares that there are no conflicts of interest regarding this work.
Références
Environ Resour Econ (Dordr). 2020;76(4):553-580
pubmed: 32836865