COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different...COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.展开更多
云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此...云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。展开更多
基金This work was supported by the Taif University Researchers supporting Project Number(TURSP-2020/254).
文摘COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.
文摘云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。
