A Novel Data-Driven Model for Real-Time Influenza Forecasting

We propose a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting. The novel aspects of the method include the following: 1) the introduction of LSTM method to capture the temporal dynamics of seasonal flu and 2) a tech...

詳細記述

書誌詳細

出版年:	IEEE Access
主要な著者:	Siva R. Venna, Amirhossein Tavanaei, Raju N. Gottumukkala, Vijay V. Raghavan, Anthony S. Maida, Stephen Nichols
フォーマット:	論文
言語:	英語
出版事項:	IEEE 2019-01-01
主題:	Influenza forecasting LSTM recurrent neural networks spatio-temporal data time series forecasting
オンライン･アクセス:	https://ieeexplore.ieee.org/document/8581423/