An Accurate Model for Covid-19 Positive Cases in India by using Traditional ARIMA and Artificial Neural Networks (LSTM and Bi-LSTM)

Indian Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 17(23), P. 2421 - 2429

Published: May 28, 2024

Objective: This study focuses on evaluating the accuracy of models that can be identified by taking data COVID-19-positive cases in India. Methods: To build using procedures, Artificial Neural Networks (ANN) and Auto Regressive Integrated Moving Average (ARIMA). The has been taken for various time periods (Covid-19 positive cases) from March 2022 to July 2023; Nov. 2023. was collected official website World Health Organization (WHO). traditional ARIMA Long Short-Term Memory (LSTM) deep learning methods were applied periods. Findings: Model performance is being measured with error parameter (Root Mean Square Error) RMSE (215.74, 100.36 127.81) respectively all LSTM performing better than a minimum value RMSE. Novelty: done Covid-19 help LSTM, Bi-LSTM methods. outcome these gave; accurate best model. Keywords: ARIMA, Networks,

Language: Английский

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

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)

Published: Aug. 27, 2024

Language: Английский