基于回归的Covid-19大流行预测

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2020-11-26 DOI:10.1109/ICRCICN50933.2020.9296175

A. Mandayam, Rakshith A.C, S. Siddesha, S. Niranjan

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引用次数: 14

摘要

随着机器学习领域的发展，预测分析已经成为未来预测的关键组成部分。在我们面临COVID-19大流行之际，预测未来阳性病例的数量将有助于更好地采取措施和控制。我们利用COVID-19的时间序列数据集，使用两个监督学习模型来预测未来。为了研究预测的性能，对线性回归和支持向量回归进行了比较。我们使用这两个模型是因为数据几乎是线性的。

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Prediction of Covid-19 pandemic based on Regression

With the progression in the field of machine learning, predictive analysis has become a key component for future prediction. As we face the COVID-19 pandemic, it would be helpful to predict the future number of positive cases for better measures and control. We used two supervised learning models to predict the future using the time-series dataset of COVID-19. To study the performance of prediction, the comparison between Linear Regression and Support Vector Regression is carried out. We have used these two models as the data were almost linear.

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来源期刊

2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

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