Comparison of Active COVID-19 Cases per Population Using Time-Series Models

被引：1

作者：

Folorunso, Sakinat Oluwabukonla ^{[1
]}

Awotunde, Joseph Bamidele ^{[2
]}

Banjo, Oluwatobi Oluwaseyi ^{[1
]}

Ogundepo, Ezekiel Adebayo ^{[3
]}

Adeboye, Nureni Olawale ^{[4
]}

机构：

[1] Olabisi Onabanjo Univ, Ago Iwoye, Nigeria

[2] Univ Iliorin, Iliorin, Nigeria

[3] Data Sci Nigeria, Lagos, Nigeria

[4] Fed Polytech, Dept Math & Stat, Ilaro, Nigeria

来源：

INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS | 2022年 / 13卷 / 02期

关键词：

ARIMA; COVID-19; Deep Learning; Epidemic; Exponential Smoothing; Forecasting; ARIMA;

D O I：

10.4018/IJEHMC.20220701.oa6

中图分类号：

R-058 [];

学科分类号：

摘要：

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the 36 different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered, and death cases as of 4 November 2020. A 14step forecast system for active coronavirus cases was built, analyzed, and compared for six different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states, respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

引用

页数：21

共 51 条

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