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Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate.<\/jats:p>","DOI":"10.3233\/jifs-231229","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:19:27Z","timestamp":1688123967000},"page":"5551-5566","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting Covid-19 outbreak using CLR optimized stacked generalization computational models"],"prefix":"10.1177","volume":"45","author":[{"given":"Saranya Devi","family":"Jeyabalan","sequence":"first","affiliation":[{"name":"Madras Institute of Technology, Anna University, Chennai, India"}]},{"given":"Nancy Jane","family":"Yesudhas","sequence":"additional","affiliation":[{"name":"Madras Institute of Technology, Anna University, Chennai, India"}]},{"given":"Jayashree","family":"Sathyanarayanan","sequence":"additional","affiliation":[{"name":"Madras Institute of Technology, Anna University, Chennai, 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