Browsing by Author "AMNA KHALIL"

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    Forecasting covid-19 vaccination trends using time series analysis based on hybrid harvest model
    (UMT Lahore, 2022) AMNA KHALIL
    There are numerous valuable worldwide works to develop and distribute vaccination. Vaccination is a huge region of the world’s populace, which is critical to controlling pestilence, presently faces another arrangement of boundaries, worldwide rivalry due to congestion, and antibodies. COVID-19 vaccination data using time series analysis techniques. A time series analysis conducted a COVID-19 vaccination dataset to forecast future trends. Four models were used, including ARIMA, Prophet, LSTM, and the proposed Hybrid Harvest model. Proposed Hybrid Harvest model, a combination of ARIMA and Prophet, was found to be the most accurate in terms of prediction accuracy with a RMSE of 0.0305 and an MSE of 0.1323. The LSTM model was not suitable for this type of data with a high RMSE and MSE. This study highlights the potential of combining models in time series analysis and the importance of considering multiple models in forecasting future trends. This Work provide new insights into the temporal patterns of COVID-19 vaccination data and can contribute to the development of more effective vaccination strategies. The results are limited to the specific dataset used and further research is needed in this area.