Proposing and Optimizing COVID-19 Predictions: A Comprehensive Ensemble Approach for Time Series Forecasting in India

• Published in: SN computer science Vol. 5; no. 7; p. 828
• Main Authors: Gupta, Aakanshi, Khan, Tooba, Mishra, Nidhi, Jatana, Nishtha, Malik, Shaily, Garg, Vaani
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.10.2024; Springer Nature B.V
• Subjects: Algorithms; Autoregressive models; Comparative analysis; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Deep learning; Disease transmission; Economic forecasting; Emergencies; Emerging Applications of Cyber-Physical System; Error analysis; Extreme values; Forecasting; Global economy; Information Systems and Communication Service; Neural networks; Original Research; Pattern Recognition and Graphics; Performance evaluation; Performance measurement; Performance prediction; Prediction models; Regression models; Root-mean-square errors; Software Engineering/Programming and Operating Systems; Statistical analysis; Time series; Trajectory analysis; Viral diseases; Viral infections; Vision; India; AR (auto regression); LSTM (long-short term memory); ARIMA (auto regression integrated moving average)
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-03209-1

The novel coronavirus (COVID-19) has devastated millions of people and is a major threat to world health. The world economy was severely disrupted, millions of people died, and many suffered from severe psychological illnesses. Therefore, by employing time-series forecasting of COVID-19 instances for projecting effective cases using time series models, the government will be better able to deal with emergencies of this kind. Values of confirmed COVID-19 instances extremely close to the actual values may be predicted using time series forecasting data. Therefore, the primary objective of the research is to identify a model that outperforms the current models in terms of prediction and is more helpful for predicting emergencies during an epidemic or a pandemic. In the latest research, time-series models such as LSTM (Long-Short Term Memory), ARIMA (Auto Regression Integrated Moving Average), AR (Auto regression), and proposed ensemble model of Lasso regression and ridge regression with gradient boost as meta model have been studied for better Covid-19 prediction. After computing performance metrics, the root-mean-squared error, or RMSE, and mean absolute error (MAE) of each model were discovered and normalized for evaluation of the performance of the prediction models. The proposed ensemble model was found to exhibit better performance than the other models in terms of prediction accuracy. Later studies will concentrate on creating novel models capable of projecting time series data in line with the trajectory of impending COVID-19 variations.