基于Adaboost的2019冠状病毒病对GDP影响预测

Ding Jiangying, Huaijin Shi, Yichen Zhang, Zhang Huiying
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引用次数: 1

摘要

Covid-19大流行对经济的影响与其他传染病一样独特。流行病影响人们的日常消费活动,例如,使他们减少购物、减少旅行、减少消费和减少投资。大量经济活动的减少导致社会需求受到抑制,消费水平降低,进而影响世界各国的GDP。为了控制和分析疫情影响下的经济形势,有必要对疫情对GDP的影响进行调查和分析。本文将COVID-19对各国GDP的影响作为回归问题,提出通过特征工程结合Aaboost模型对GDP进行预测。该模型在Kaggle平台提供的200多个国家的5万多条数据记录上进行了测试,以证明其有效性。实验表明,与随机森林、SVR等方法相比,Adaboost具有更强的鲁棒性。Adaboost将随机森林的MSE提高了2.39,SVR提高了0.38。
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 on GDP based on Adaboost
Covid-19 Pandemic has a unique impact on the economy as other infectious diseases. Epidemics affect people's daily consumption activities, for example, by causing them to shop less, travel less, consume less and invest less. The reduction of a large number of economic activities leads to the suppression of social demand and the reduction of consumption level, which further affects the GDP of various countries around the world. It is necessary to investigate and analyze the impact of the epidemic on GDP in order to control and analyze the economic situation under the impact of the epidemic. In this paper, we take the impact of COVID-19 on the GDP of each country as a regression problem, and propose to forecast GDP through feature engineering combined with Aaboost model. The model was tested on more than 50,000 data records from more than 200 countries provided by the Kaggle platform to prove the validity. The experiment shows that Adaboost has stronger robustness compared with other methods, such as random forest, SVR. Adaboost improves the MSE of random forest by 2.39 and SVR by 0.38.
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