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Syerrina Zakaria

As seen in: PLOS ONE, MDPI
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Articles by Syerrina Zakaria

Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022

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By Universiti Malaysia Terengganu, Kuala Nerus, Muhamad Safiih Lola, Syerrina Zakaria
| PLOS ONE
Proposed hybrid models Despite various time series models presented, the accuracy, effectively as well as precisely of time series forecasting at this time become the fundamental to many decision-making processes. However, those factors do not occur in the ARIMA and SVM models. This also become the most reason why time series forecasting model is crucial, most challenging, and dynamic as well as active research in many fields of studies.
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Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

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By Wan Azani Mustafa, Muhamad Safiih Lola, Syerrina Zakaria, Elayaraja Aruchunan
| MDPI
3.1. Application of the Hybrid Model of COVID-19 in Malaysia This section examined the proposed model’s performance in two ways: first, the performance of the proposed models compared to ARIMA, SVM, LSSVM, ARIMA–SVM, and ARIMA–LSSVM models; second, the percentage improvement of the proposed models compared to ARIMA and SVM models. Since the World Health Organization (WHO) declared COVID-19 to be a worldwide pandemic, the COVID-19 time-series datasets have been extensively studied.
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