利用ARIMA和LSTM预测印度尼西亚、马来西亚、菲律宾和越南的COVID-19病例

Marina Wahyuni Paedah, F. Gunawan
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引用次数: 0

摘要

新冠肺炎疫情对包括东盟国家在内的世界经济造成严重冲击。在大流行到流行病的过渡期间,仍需要制定各种计划和战略,以尽量减少COVID-19传播的风险。此次研究的重点是东南亚地区确诊病例最多的东盟国家印度尼西亚、马来西亚、菲律宾、越南的确诊病例总数。这些国家在文化上有相似之处,与朋友和家人聚会是社会生活的重要组成部分。本研究利用2020年1月23日至2022年10月22日的每日数据,评估了ARIMA和LSTM预测各国COVID-19病例的能力。使用了约翰霍普金斯大学(JHU)和我们的数据世界(OWID)发布的数据集,这些数据集可以通过Github访问。与ARIMA模型相比,印度尼西亚的R2为0.8883,马来西亚的R2为0.8353,菲律宾的R2为0.97291,越南的R2为-3.105,LSTM模型对四个东盟国家的预测效果更好,印度尼西亚的R2为0.9996,马来西亚的R2为0.9707,菲律宾的R2为0.97291,越南的R2为0.9200。
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Forecasting COVID-19 Cases in Indonesia, Malaysia, Philippines, and Vietnam Using ARIMA and LSTM
COVID-19 has severely impacted the global economy, including ASEAN countries. Various plans and strategies are still needed during the pandemic-to-epidemic transition period to minimize the risk of COVID-19 transmission. The research focuses on the total number of confirmed cases of COVID-19 in Indonesia, Malaysia, the Philippines, and Vietnam, which are among the ASEAN countries with the highest number of cases in Southeast Asia. Those countries have cultural similarities, where gathering with friends and family is an important part of social life. This research evaluates the ability of ARIMA and LSTM to predict COVID-19 cases in each country, using daily data from January 23, 2020 to October 22, 2022. Datasets published by Johns Hopkins University (JHU) and Our World in Data (OWID) are used, which are accessible through Github. Compared to ARIMA with  R2 of 0,8883 for Indonesia, 0,8353 for Malaysia, 0.97291 for the Philippines, and -3.105 for Vietnam, LSTM model can predict better in the four sampled ASEAN countries, with an R2 of 0.9996 for Indonesia, 0.9707 for Malaysia, 0.97291 for the Philippines, and 0.9200 for Vietnam.
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