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Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to other countries including Malaysia. COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. The number o...

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Main Author: Ramli, Siti Nor Idayu
Format: Student Project
Language:English
Published: 2021
Subjects:
Prediction analysis
Time-series analysis
Online Access:https://ir.uitm.edu.my/id/eprint/49303/1/49303.pdf
https://ir.uitm.edu.my/id/eprint/49303/
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spelling my.uitm.ir.493032021-08-30T08:39:13Z https://ir.uitm.edu.my/id/eprint/49303/ Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli Ramli, Siti Nor Idayu Prediction analysis Time-series analysis In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to other countries including Malaysia. COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. The number of confirmed cases of COVID-19 is increasing daily and reached to 673 cases on March 17, 2020. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. Therefore, this study is conducted to predict the new confirmed cases of COVID-19 in Malaysia using univariate time series models. The models used are Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, and Exponential State Space models. The data used for this study is collected from January 25 to November 23, 2020 from the Ministry of Health Malaysia’s website. Five different sets of data partitioning will be used to ensure the accuracy of the forecast values generated. R-programming was used to analyze the data for this study. The results indicates that Naïve model with partitioning set of 95 percent (Estimation Part) and 5 percent (Evaluation Part) shown a better performance in terms of Mean Error (ME), Root Mean Squared Error (RMSE), and Mean Percentage Error (MPE). However, its performance will decline whenever there is a random outburst in the data. 2021-08-04 Student Project NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/49303/1/49303.pdf ID49303 Ramli, Siti Nor Idayu (2021) Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli. [Student Project] (Unpublished)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Prediction analysis
Time-series analysis
spellingShingle Prediction analysis
Time-series analysis
Ramli, Siti Nor Idayu
Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
description In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to other countries including Malaysia. COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. The number of confirmed cases of COVID-19 is increasing daily and reached to 673 cases on March 17, 2020. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. Therefore, this study is conducted to predict the new confirmed cases of COVID-19 in Malaysia using univariate time series models. The models used are Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, and Exponential State Space models. The data used for this study is collected from January 25 to November 23, 2020 from the Ministry of Health Malaysia’s website. Five different sets of data partitioning will be used to ensure the accuracy of the forecast values generated. R-programming was used to analyze the data for this study. The results indicates that Naïve model with partitioning set of 95 percent (Estimation Part) and 5 percent (Evaluation Part) shown a better performance in terms of Mean Error (ME), Root Mean Squared Error (RMSE), and Mean Percentage Error (MPE). However, its performance will decline whenever there is a random outburst in the data.
format Student Project
author Ramli, Siti Nor Idayu
author_facet Ramli, Siti Nor Idayu
author_sort Ramli, Siti Nor Idayu
title Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
title_short Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
title_full Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
title_fullStr Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
title_full_unstemmed Prediction of new confirmed cases of covid-19 in Malaysia using univariate time series models / Siti Nor Idayu Ramli
title_sort prediction of new confirmed cases of covid-19 in malaysia using univariate time series models / siti nor idayu ramli
publishDate 2021
url https://ir.uitm.edu.my/id/eprint/49303/1/49303.pdf
https://ir.uitm.edu.my/id/eprint/49303/
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score 13.211869

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