Predicting Impact of COVID-19 on the Global Economy Based on Hybrid Model

DOI

10.2991/aebmr.k.220307.315How to use a DOI?

Keywords

economy; COVID-19; hybrid model; AdaBoost; Linear Regression; Decision Tree; MSE

Abstract

The outbreak of COVID-19 has caused hitherto unknown damage to the global economy, resulting in a large number of company failures and staff unemployment. In order to suppress the further spread of the virus, many countries and regions have adopted quarantine policies to varying degrees. This has led to the failure of normal business activities and financial market behaviors. The health crisis caused by the new crown pneumonia has gradually turned into an economic crisis. In this paper, the data provided by the KAGGLE competition platform is used to analyze and predict the impact of COVID-19 on the economy. A mixed prediction model (AdaBoost, Linear Regression, Decision Tree) is constructed to predict the GDP per capita of different countries. The results show that the hybrid model has the highest accuracy. Specifically, the lowest MSE index score of our model is 7.57, and KNN and decision tree algorithm are 2.23 and 2.19 higher than our hybrid model respectively.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Jincheng Zuo
AU  - Fenglan Ma
AU  - Shaun Chen
PY  - 2022
DA  - 2022/03/26
TI  - Predicting Impact of COVID-19 on the Global Economy Based on Hybrid Model
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 1918
EP  - 1922
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.315
DO  - 10.2991/aebmr.k.220307.315
ID  - Zuo2022
ER  -