Time series forecasting for tourism industry in Malaysia
This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Pushpa Publishing House
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/43502/ http://umpir.ump.edu.my/id/eprint/43502/1/2024-ADAS%20-%20SARIMA%20Tourism.pdf |
| _version_ | 1848826891916541952 |
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| author | Noratikah, Abu Siti Aishah @Tsamienah, Taib Nurul Amira, Zainal Nor Azuana, Ramli Go, Clark Kendrick |
| author_facet | Noratikah, Abu Siti Aishah @Tsamienah, Taib Nurul Amira, Zainal Nor Azuana, Ramli Go, Clark Kendrick |
| author_sort | Noratikah, Abu |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate the best Box-Jenkins model before proceeding to forecasting stage are MAPE and RMSE, and the analysis was performed by using Python. The findings show that SARIMA (2,1,1)(0,1,1)12 was considered as highly accurate forecasting model based on its least error produced. |
| first_indexed | 2025-11-15T03:52:02Z |
| format | Article |
| id | ump-43502 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:52:02Z |
| publishDate | 2024 |
| publisher | Pushpa Publishing House |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-435022025-01-10T08:15:34Z http://umpir.ump.edu.my/id/eprint/43502/ Time series forecasting for tourism industry in Malaysia Noratikah, Abu Siti Aishah @Tsamienah, Taib Nurul Amira, Zainal Nor Azuana, Ramli Go, Clark Kendrick QA Mathematics This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate the best Box-Jenkins model before proceeding to forecasting stage are MAPE and RMSE, and the analysis was performed by using Python. The findings show that SARIMA (2,1,1)(0,1,1)12 was considered as highly accurate forecasting model based on its least error produced. Pushpa Publishing House 2024-11-09 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/43502/1/2024-ADAS%20-%20SARIMA%20Tourism.pdf Noratikah, Abu and Siti Aishah @Tsamienah, Taib and Nurul Amira, Zainal and Nor Azuana, Ramli and Go, Clark Kendrick (2024) Time series forecasting for tourism industry in Malaysia. Advances and Applications in Statistics, 92 (1). pp. 77-87. ISSN 0972-3617. (Published) https://doi.org/10.17654/0972361725004 https://doi.org/10.17654/0972361725004 |
| spellingShingle | QA Mathematics Noratikah, Abu Siti Aishah @Tsamienah, Taib Nurul Amira, Zainal Nor Azuana, Ramli Go, Clark Kendrick Time series forecasting for tourism industry in Malaysia |
| title | Time series forecasting for tourism industry in Malaysia |
| title_full | Time series forecasting for tourism industry in Malaysia |
| title_fullStr | Time series forecasting for tourism industry in Malaysia |
| title_full_unstemmed | Time series forecasting for tourism industry in Malaysia |
| title_short | Time series forecasting for tourism industry in Malaysia |
| title_sort | time series forecasting for tourism industry in malaysia |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/43502/ http://umpir.ump.edu.my/id/eprint/43502/ http://umpir.ump.edu.my/id/eprint/43502/ http://umpir.ump.edu.my/id/eprint/43502/1/2024-ADAS%20-%20SARIMA%20Tourism.pdf |