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Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran

The new virus and disease have been found (COVID-19), with the outbreak started in December 2019 in Wuhan, unknown China. Coronavirus disease 2019 is a global pandemic that affects many countries. The number of confirmed cases worldwide on 4 May 2020 is 3,435,894 cases, with 239,604 deaths. On 25 Ma...

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Main Author: Raja Azran, Raja Azizul Fitri
Format: Student Project
Language:English
Published: 2021
Subjects:
Mathematical statistics. Probabilities
Time-series analysis
RB Pathology
Online Access:http://ir.uitm.edu.my/id/eprint/45076/1/45076.pdf
http://ir.uitm.edu.my/id/eprint/45076/
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spelling my.uitm.ir.450762021-06-22T03:54:09Z http://ir.uitm.edu.my/id/eprint/45076/ Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran Raja Azran, Raja Azizul Fitri Mathematical statistics. Probabilities Time-series analysis RB Pathology The new virus and disease have been found (COVID-19), with the outbreak started in December 2019 in Wuhan, unknown China. Coronavirus disease 2019 is a global pandemic that affects many countries. The number of confirmed cases worldwide on 4 May 2020 is 3,435,894 cases, with 239,604 deaths. On 25 March 2020, China recorded the highest number of confirmed cases with 81,848 cases and 3,827 deaths. The new confirmed cases keep on increasing day by day. Therefore, this study is conducted to forecast the new confirmed cases of COVID-19 in Hubei, China for a short-term period by using the Naïve method, Mean model, Autoregressive Integrated Moving Average (ARIMA), and State space model. There are 9 different training sets and test sets for every method in this study. All the methods are also divided into 4 datasets in which each dataset will predict a 3-step ahead forecast by using the best model that produces the least error measure. The result shows that the Naïve method is the best model for all 4 datasets since it produces the lowest error measure. However, the prediction of new confirmed cases by the Naïve method is not accurate to the actual new confirmed cases from the Kaggle website. 2021-04-09 Student Project NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/45076/1/45076.pdf ID45076 Raja Azran, Raja Azizul Fitri (2021) Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran. [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 Mathematical statistics. Probabilities
Time-series analysis
RB Pathology
spellingShingle Mathematical statistics. Probabilities
Time-series analysis
RB Pathology
Raja Azran, Raja Azizul Fitri
Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
description The new virus and disease have been found (COVID-19), with the outbreak started in December 2019 in Wuhan, unknown China. Coronavirus disease 2019 is a global pandemic that affects many countries. The number of confirmed cases worldwide on 4 May 2020 is 3,435,894 cases, with 239,604 deaths. On 25 March 2020, China recorded the highest number of confirmed cases with 81,848 cases and 3,827 deaths. The new confirmed cases keep on increasing day by day. Therefore, this study is conducted to forecast the new confirmed cases of COVID-19 in Hubei, China for a short-term period by using the Naïve method, Mean model, Autoregressive Integrated Moving Average (ARIMA), and State space model. There are 9 different training sets and test sets for every method in this study. All the methods are also divided into 4 datasets in which each dataset will predict a 3-step ahead forecast by using the best model that produces the least error measure. The result shows that the Naïve method is the best model for all 4 datasets since it produces the lowest error measure. However, the prediction of new confirmed cases by the Naïve method is not accurate to the actual new confirmed cases from the Kaggle website.
format Student Project
author Raja Azran, Raja Azizul Fitri
author_facet Raja Azran, Raja Azizul Fitri
author_sort Raja Azran, Raja Azizul Fitri
title Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
title_short Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
title_full Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
title_fullStr Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
title_full_unstemmed Short-term forecast of new confirm case of COVID-19 in Hubei, China using naive method, mean model, autoregressive integrated moving average [ARIMA] and state space model / Raja Azizul Fitri Raja Azran
title_sort short-term forecast of new confirm case of covid-19 in hubei, china using naive method, mean model, autoregressive integrated moving average [arima] and state space model / raja azizul fitri raja azran
publishDate 2021
url http://ir.uitm.edu.my/id/eprint/45076/1/45076.pdf
http://ir.uitm.edu.my/id/eprint/45076/
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score 13.211869

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