An overview of forecast analysis with ARIMA models during the COVID-19 pandemic: Methodology and case study in Brazil
This comprehensive overview focuses on the issues presented by the pandemic due to
COVID-19, understanding its spread and the wide-ranging effects of government-imposed …
COVID-19, understanding its spread and the wide-ranging effects of government-imposed …
Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review
COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the
rate of COVID-19 spread and modeling of its course have critical impact on both health …
rate of COVID-19 spread and modeling of its course have critical impact on both health …
[HTML][HTML] Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal …
Several machine learning and deep learning models were reported in the literature to
forecast COVID-19 but there is no comprehensive report on the comparison between …
forecast COVID-19 but there is no comprehensive report on the comparison between …
A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models
This study aims to develop an assumption-free data-driven model to accurately forecast
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the …
Predicting the impact of the third wave of COVID-19 in India using hybrid statistical machine learning models: A time series forecasting and sentiment analysis …
Abstract Background Since January 2020, India has faced two waves of COVID-19;
preparation for the upcoming waves is the primary challenge for public health sectors and …
preparation for the upcoming waves is the primary challenge for public health sectors and …
Computational machine learning approach for flood susceptibility assessment integrated with remote sensing and GIS techniques from Jeddah, Saudi Arabia
Floods, one of the most common natural hazards globally, are challenging to anticipate and
estimate accurately. This study aims to demonstrate the predictive ability of four ensemble …
estimate accurately. This study aims to demonstrate the predictive ability of four ensemble …
A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning
W Jin, S Dong, C Yu, Q Luo - Computers in Biology and Medicine, 2022 - Elsevier
The COVID-19 outbreak poses a huge challenge to international public health. Reliable
forecast of the number of cases is of great significance to the planning of health resources …
forecast of the number of cases is of great significance to the planning of health resources …
MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions
R Zhang, X Ma, W Ding, J Zhan - Information Sciences, 2023 - Elsevier
Currently, prediction stands as one of the most prominent areas of research. Enhancing the
accuracy and generalization capabilities of prediction models remains a crucial and ongoing …
accuracy and generalization capabilities of prediction models remains a crucial and ongoing …
Twitter conversations predict the daily confirmed COVID-19 cases
As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220
countries and territories. Following the outbreak, the pandemic's seriousness has made …
countries and territories. Following the outbreak, the pandemic's seriousness has made …
A novel grey seasonal model with time power for energy prediction
W Zhou, J Chang, H Jiang, S Ding, R Jiang… - Expert Systems with …, 2025 - Elsevier
The fluctuation of seasonal time series has attracted considerable attention. However, the
complexity of socio-economic development and the variability of influencing factors make …
complexity of socio-economic development and the variability of influencing factors make …