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新冠疫情预测模型的交叉验证比较

Cross-Validation Comparison of COVID-19 Forecast Models.

作者信息

Atchadé Mintodê Nicodème, Sokadjo Yves Morel, Moussa Aliou Djibril, Kurisheva Svetlana Vladimirovna, Bochenina Marina Vladimirovna

机构信息

National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin.

University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), Abomey-Calavi , Republic of Benin.

出版信息

SN Comput Sci. 2021;2(4):296. doi: 10.1007/s42979-021-00699-1. Epub 2021 May 26.

DOI:10.1007/s42979-021-00699-1
PMID:34056624
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8150153/
Abstract

Many papers have proposed forecasting models and some are accurate and others are not. Due to the debatable quality of collected data about COVID-19, this study aims to compare univariate time series models with cross-validation and different forecast periods to propose the best one. We used the data titled "Coronavirus Pandemic (COVID-19)" from "'Our World in Data" about cases for the period of 31 December 2019 to 21 November 2020. The Mean Absolute Percentage Error (MAPE) is computed per model to make the choice of the best fit. Among the univariate models, Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA; we got that the best one is ETS with additive error-trend and no season. The findings revealed that with the ETS model, we need at least 100 days to have good forecasts with a MAPE threshold of 5%.

摘要

许多论文都提出了预测模型，有些模型准确，有些则不然。由于关于新冠疫情收集的数据质量存在争议，本研究旨在比较具有交叉验证和不同预测期的单变量时间序列模型，以提出最佳模型。我们使用了来自“Our World in Data”的名为“冠状病毒大流行（COVID-19）”的数据，涵盖2019年12月31日至2020年11月21日期间的病例。为了选择最佳拟合模型，我们计算了每个模型的平均绝对百分比误差（MAPE）。在单变量模型中，包括误差趋势季节模型（ETS）、具有乘法误差趋势的指数平滑模型和自回归整合移动平均模型（ARIMA）；我们发现最佳模型是具有加法误差趋势且无季节因素的ETS模型。研究结果表明，使用ETS模型时，我们至少需要100天才能在MAPE阈值为5%的情况下获得良好的预测结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/62630cca964d/42979_2021_699_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/3a4c50c14ba7/42979_2021_699_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/c5a77540b63f/42979_2021_699_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/2e61e1be1f3c/42979_2021_699_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/62630cca964d/42979_2021_699_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/3a4c50c14ba7/42979_2021_699_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/c5a77540b63f/42979_2021_699_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/2e61e1be1f3c/42979_2021_699_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eec9/8150153/62630cca964d/42979_2021_699_Fig4_HTML.jpg

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