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An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

Meilin Wu1,2, Lianggui Tang1,2,*, Qingda Zhang1,2, Ke Yan1,2

1 The Artificial Intelligence College, Chongqing Technology and Business University, Chongqing, 400067, China
2 Chongqing Key Laboratory of IntelliSense and Blockchain Technology, Chongqing, 400067, China

* Corresponding Author: Lianggui Tang. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 179-198.


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.


Cite This Article

M. Wu, L. Tang, Q. Zhang and K. Yan, "An improved granulated convolutional neural network data analysis model for covid-19 prediction," Intelligent Automation & Soft Computing, vol. 37, no.1, pp. 179–198, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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