Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia
On March 11,2020, the World Health Organization (WHO) declared Covid-19 as a global pandemic. The spread of Covid-19 has threatened many lives in nearly every country. In Malaysia, the health authorities have expressed concerns over an increasing number of cases and deaths. Due to the lockdown, thi...
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| Format: | Article |
| Language: | English |
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2022-09-01
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| Series: | Journal of Computing Research and Innovation |
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| Online Access: | https://jcrinn.com/index.php/jcrinn/article/view/298 |
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| author | Aina Humaira Rostam Nor Hayati Binti Shafii Nur Fatihah Fauzi Diana Sirmayunie Md Nasir Nor Azriani Mohamad Nor |
| author_facet | Aina Humaira Rostam Nor Hayati Binti Shafii Nur Fatihah Fauzi Diana Sirmayunie Md Nasir Nor Azriani Mohamad Nor |
| author_sort | Aina Humaira Rostam |
| collection | DOAJ |
| description |
On March 11,2020, the World Health Organization (WHO) declared Covid-19 as a global pandemic. The spread of Covid-19 has threatened many lives in nearly every country. In Malaysia, the health authorities have expressed concerns over an increasing number of cases and deaths. Due to the lockdown, this pandemic has also had an impact on most economic activities. Consequently, it is crucial to develop a reliable forecasting model to anticipate the number of cases. This study proposes two models: Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MPNN) in predicting the number of Covid-19 cases in Malaysia. Using Mean Absolute Error (MAE), the effectiveness and forecasting accuracy of the two models are compared and assessed. The lowest the value of MAE, the more accurate the forecasted outputs. The secondary data used in this study was the average number of Covid-19 cases each day in Malaysia from March 1, 2020, to March 29, 2021. To evaluate the data, RStudio and Alyuda NeuroIntelligence are utilised. As a consequence, the ARIMA (4,1,5) model provided the best fit to the data when compared to other ARIMA models, with a Mean Absolute Error (MAE) score of 1096.799. However, Multilayer Perceptron Neural Network (MPNN), which had the lowest MAE value of 334.591, outperformed ARIMA in terms of performance. The MPNN model was then used to forecast the number of Covid-19 instances for the next 30 days. According to the findings, daily increases in cases are anticipated.
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| first_indexed | 2024-03-11T13:37:22Z |
| format | Article |
| id | doaj.art-6633055a5ce64aea8639e672e49e47fc |
| institution | Directory Open Access Journal |
| issn | 2600-8793 |
| language | English |
| last_indexed | 2024-03-11T13:37:22Z |
| publishDate | 2022-09-01 |
| publisher | Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
| record_format | Article |
| series | Journal of Computing Research and Innovation |
| spelling | doaj.art-6633055a5ce64aea8639e672e49e47fc2023-11-02T15:09:56ZengFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisJournal of Computing Research and Innovation2600-87932022-09-0172Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in MalaysiaAina Humaira Rostam0Nor Hayati Binti Shafii1Nur Fatihah Fauzi2Diana Sirmayunie Md Nasir3Nor Azriani Mohamad Nor4Universiti Teknologi MARA, Cawangan PerlisMrsUniversiti Teknologi MARA, Cawangan PerlisUniversiti Teknologi MARA, Cawangan PerlisUniversiti Teknologi MARA, Cawangan Perlis On March 11,2020, the World Health Organization (WHO) declared Covid-19 as a global pandemic. The spread of Covid-19 has threatened many lives in nearly every country. In Malaysia, the health authorities have expressed concerns over an increasing number of cases and deaths. Due to the lockdown, this pandemic has also had an impact on most economic activities. Consequently, it is crucial to develop a reliable forecasting model to anticipate the number of cases. This study proposes two models: Autoregressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MPNN) in predicting the number of Covid-19 cases in Malaysia. Using Mean Absolute Error (MAE), the effectiveness and forecasting accuracy of the two models are compared and assessed. The lowest the value of MAE, the more accurate the forecasted outputs. The secondary data used in this study was the average number of Covid-19 cases each day in Malaysia from March 1, 2020, to March 29, 2021. To evaluate the data, RStudio and Alyuda NeuroIntelligence are utilised. As a consequence, the ARIMA (4,1,5) model provided the best fit to the data when compared to other ARIMA models, with a Mean Absolute Error (MAE) score of 1096.799. However, Multilayer Perceptron Neural Network (MPNN), which had the lowest MAE value of 334.591, outperformed ARIMA in terms of performance. The MPNN model was then used to forecast the number of Covid-19 instances for the next 30 days. According to the findings, daily increases in cases are anticipated. https://jcrinn.com/index.php/jcrinn/article/view/298ARIMAMultilayer Peceptron Neural NetworkTime-Series forecastingCOVID-19Autoregressive Integrated Moving Average |
| spellingShingle | Aina Humaira Rostam Nor Hayati Binti Shafii Nur Fatihah Fauzi Diana Sirmayunie Md Nasir Nor Azriani Mohamad Nor Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia Journal of Computing Research and Innovation ARIMA Multilayer Peceptron Neural Network Time-Series forecasting COVID-19 Autoregressive Integrated Moving Average |
| title | Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia |
| title_full | Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia |
| title_fullStr | Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia |
| title_full_unstemmed | Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia |
| title_short | Autoregressive Integrated Moving Average vs. Artificial Neural Network in Predicting COVID-19 Cases in Malaysia |
| title_sort | autoregressive integrated moving average vs artificial neural network in predicting covid 19 cases in malaysia |
| topic | ARIMA Multilayer Peceptron Neural Network Time-Series forecasting COVID-19 Autoregressive Integrated Moving Average |
| url | https://jcrinn.com/index.php/jcrinn/article/view/298 |
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