基于不同时间序列模型预测新冠肺炎疫情对中国出口的影响

Q2 Decision Sciences
S. Safi, O. I. Sanusi, M. I. Tabash
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引用次数: 4

摘要

目的:本文的主要目标是确定中国出口的最佳预测模型,特别是在COVID-19大流行蔓延期间。方法:利用2014年1月至2021年1月中国对美出口及不同经济区域的出口数据,对不同标准下的模型进行比较,选出最佳的出口预测模型。采用混合模型进行分析。混合模型组合由ARIMA、ETS、Theta、NNAR、季节和趋势分解、TBATS模型等6个模型组成。研究结果表明,考虑到持续的冠状病毒大流行带来的冲击,混合模型和人工神经网络在预测中国对世界的出口方面优于其他模型。本文强调了使用特定模型预测这一时期出口的重要性。结果还表明,中国对所有集团的出口规模都有所下降,并将在未来几个月继续下降。实际意义:对未来9个月的出口数据进行预测,从而为所有政策制定者、政府和投资者提供见解,以积极主动地设计策略,避免从中国进口的任何延迟/中断,这可以增强原材料的顺畅流动,并维持工业生产。©2022 Hindawi Limited。版权所有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting the Impact of COVID-19 Epidemic on China Exports using Different Time Series Models
Purpose: The primary objective of this paper is to identify the best forecasting model for China exports, especially during the spread of the COVID-19 pandemic. Methodology: We used the data of China exports to the United States and different economic regions from January 2014 to January 2021 to compare models using various criteria and selected the best exports forecast model. The hybrid model is employed to conduct the analysis. The combination of the hybrid model consists of six different models: ARIMA, ETS, Theta, NNAR, seasonal and trend decomposition, and TBATS model. Findings: Our results showed that the hybrid and ANN outperformed the remaining models in forecasting China exports to the world, considering the shock created by the ongoing coronavirus pandemic. This paper underscores the importance of using the specified models in forecasting exports during this period. The results also demonstrate that the magnitude of China exports to all groups decreased and will continue to decline for the next few months. Practical Implication: Forecasting of the export data is presented for the subsequent nine months, thereby providing insights to all policymakers, governments, and investors to be proactive in designing their strategies to avoid any delay/disruption in the imports from China, which could enhance the smooth flow of raw material and sustain industrial production. © 2022 Hindawi Limited. All rights reserved.
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来源期刊
Advances in Decision Sciences
Advances in Decision Sciences Mathematics-Applied Mathematics
CiteScore
4.70
自引率
0.00%
发文量
18
审稿时长
29 weeks
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