基于LSTM的深度学习模型对印度COVID-19疫情的时间序列预测

Ameet, Chhavi Rana
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引用次数: 0

摘要

新冠肺炎疫情对人类文明的延续构成重大威胁,已经给社会造成无法弥补的损害。本文采用LSTM和CovnLSTM深度神经网络技术对印度冠状病毒疫情进行预测。使用印度确诊病例的COVID-19数据。这是约翰霍普金斯大学的照片。ConvLSTM的损失率低于LSTM,其RMSE也低于LSTM。在训练和测试方面,ConvLSTM模型比LSTM模型提高了0.069%,比LSTM模型提高了0.32%。因此,ConvLSTM优于LSTM模型。进一步明智地选择超参数可以提高模型的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time series prediction of the COVID-19 outbreak in India using LSTM based deep learning models
The epidemic caused by COVID-19 presents a significant risk to the continuation of human civilisation and has already done irreparable damage to society. In this paper, forecasting of Coronavirus outbreak in India is performed by LSTM and CovnLSTM deep neural network techniques. COVID-19 data of confirmed cases of India is used. It was taken from John Hopkins University. The loss rate of ConvLSTM is lower than LSTM and RMSE of ConvLSTM is lower than LSTM. For training Covn-LSTM shows 0.069% and testing ConvLSTM shows 0.32% improvement over LSTM model. Therefore, ConvLSTM outperformed over LSTM model. Further wise selection of hyper-parameters could increase the accuracy of the models.
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