TY  - EJOU
AU  - Wu, Meilin 
AU  - Tang, Lianggui 
AU  - Zhang, Qingda 
AU  - Yan, Ke 

TI  - An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 1
SN  - 2326-005X

AB  - 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.
KW  - Time series forecasting; granulated convolutional networks; data analysis techniques; non-stationarity

DO  - 10.32604/iasc.2023.036684