使用时间序列模型预测、可视化和分析印度COVID-19

Afzal Ansari, Sourabh Kumar Burnwal
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引用次数: 1

摘要

自新冠肺炎疫情在中国发生并蔓延至全球以来,人类处于危险之中,并在全国范围内敲响了巨大的警钟。由于病例数量的空前增长速度及其对全球管理和卫生专业人员的后续压力,非常需要通过使用一些预测方法分析和预测新病例的数量来拥有一个安全的未来。印度目前的局势日益恶化,因此这个国家的经济一直在下降和不稳定。在本文中,我们分析了印度每日感染病例的数量,预测了趋势,并调查了目前可能达到的峰值。我们使用数据驱动的估计方法,如Fb-Prophet和长短期记忆(LSTM)作为最先进的方法和深度学习模型,分别预测未来几天印度的COVID-19病例数。我们提出了一种考虑各种参数的方法,可以在一定范围内预测未来的每日确诊病例,这对行政人员和卫生官员来说是一个有益的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting, visualization and analysis of COVID-19 in India using time series modeling
: Since the origination of COVID-19 in China and its spread across the globe, humanity has been put at risk and it has set a big alarm till its end across the country. Due to the unprecedented rate of increase in the number of cases and its subsequent pressure on the administration and health professionals globally, it would be highly needed to have a safe future by doing analysis and forecasting the number of new cases using some prediction methods. The current situation in India is getting worsened day-by-day due to which, the economy of this country has been down and unstable. In this paper, we have analyzed, how the numbers of daily infected cases in India could look like, predicting the trend, and investigate what the peak value could hit by now. We have used data-driven estimation methods like Fb-Prophet and long short-term memory (LSTM) as a state-of-the-art method and Deep Learning models respectively for forecasting the number of COVID-19 cases in India a few days ahead. We have proposed a method considering various parameters to predict daily confirmed future cases within a certain range which would be a beneficial tool for administrators and health officials.
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