@Article{iasc.2023.036684,
AUTHOR = {Meilin Wu, Lianggui Tang, Qingda Zhang, Ke Yan},
TITLE = {An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {1},
PAGES = {179--198},
URL = {http://www.techscience.com/iasc/v37n1/52666},
ISSN = {2326-005X},
ABSTRACT = {As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.},
DOI = {10.32604/iasc.2023.036684}
}