Multi-model tourist forecasting: case study of Kurdistan Region of Iraq
The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the numbe...
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| Format: | Article |
| Language: | English |
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LLC "CPC "Business Perspectives"
2019-08-01
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| Series: | Tourism & Travelling |
| Subjects: | |
| Online Access: | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/12341/TT_2019_01_Rasul.pdf |
| _version_ | 1818116764154724352 |
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| author | Azad Rasul Amanj Ahmad Hamdamin Dewana Saadaldeen Muhammad Nuri Saed |
| author_facet | Azad Rasul Amanj Ahmad Hamdamin Dewana Saadaldeen Muhammad Nuri Saed |
| author_sort | Azad Rasul |
| collection | DOAJ |
| description | The tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022. |
| first_indexed | 2024-12-11T04:27:42Z |
| format | Article |
| id | doaj.art-0b9fa89bd0af41c7860b92867dee2483 |
| institution | Directory Open Access Journal |
| issn | 2544-2295 2616-5090 |
| language | English |
| last_indexed | 2024-12-11T04:27:42Z |
| publishDate | 2019-08-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Tourism & Travelling |
| spelling | doaj.art-0b9fa89bd0af41c7860b92867dee24832022-12-22T01:20:57ZengLLC "CPC "Business Perspectives"Tourism & Travelling2544-22952616-50902019-08-0121243410.21511/tt.2(1).2019.0412341Multi-model tourist forecasting: case study of Kurdistan Region of IraqAzad Rasul0https://orcid.org/0000-0001-5141-0577Amanj Ahmad Hamdamin Dewana1https://orcid.org/0000-0002-3597-3422Saadaldeen Muhammad Nuri Saed2https://orcid.org/0000-0002-1914-8406Ph.D., Lecturer, Department of Geography, Faculty of Arts, Soran UniversityPh.D., Assistant Professor, Department of Geography, Faculty of Arts, Soran UniversityPh.D., Lecturer, Department of Geography, Faculty of Arts, Soran UniversityThe tourism industry has been one of the leading service industries in the global economy in recent years and the number of international tourism in 2018 reached 1.4 billion. The goal of the research is to evaluate the performance of various methods for forecasting tourism data and predict the number of tourists during 2019 and 2022. Performance of 15 prediction models (i.e. Local linear structural, Naïve, Holt, Random walk, ARIMA) was compared. Based on error measurements matrix (i.e. RMSE, MAE, MAPE, MASE), the most accurate method was selected to forecast the total number of tourists from 2019 to 2022 to Kurdistan Region (KR), then forecasts were performed for each governorate in KR. The results show that among 15 examined models of tourist forecasting in KR, Local linear structural and ARIMA (7,3,0) model performed best. The number of tourists to KR and each governorate in KR is predicted to increase by most experimented models, especially those which demonstrated higher accuracy. Generally, the number of tourist to KR predicted by ARIMA (7,3,0) is a lot bigger than Local linear structure. Linear structural predicted the number increase to 3,137,618 and 3,462,348 in 2020 and 2022, respectively, while ARIMA (7,3,0) predicted the number of tourists to KR to increase rapidly to 3,748,416 and 8,681,398 in 2020 and 2022.https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/12341/TT_2019_01_Rasul.pdfARIMAforecastingKurdistan Region (KR)Linear structural modelmodelingtourism |
| spellingShingle | Azad Rasul Amanj Ahmad Hamdamin Dewana Saadaldeen Muhammad Nuri Saed Multi-model tourist forecasting: case study of Kurdistan Region of Iraq Tourism & Travelling ARIMA forecasting Kurdistan Region (KR) Linear structural model modeling tourism |
| title | Multi-model tourist forecasting: case study of Kurdistan Region of Iraq |
| title_full | Multi-model tourist forecasting: case study of Kurdistan Region of Iraq |
| title_fullStr | Multi-model tourist forecasting: case study of Kurdistan Region of Iraq |
| title_full_unstemmed | Multi-model tourist forecasting: case study of Kurdistan Region of Iraq |
| title_short | Multi-model tourist forecasting: case study of Kurdistan Region of Iraq |
| title_sort | multi model tourist forecasting case study of kurdistan region of iraq |
| topic | ARIMA forecasting Kurdistan Region (KR) Linear structural model modeling tourism |
| url | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/12341/TT_2019_01_Rasul.pdf |
| work_keys_str_mv | AT azadrasul multimodeltouristforecastingcasestudyofkurdistanregionofiraq AT amanjahmadhamdamindewana multimodeltouristforecastingcasestudyofkurdistanregionofiraq AT saadaldeenmuhammadnurisaed multimodeltouristforecastingcasestudyofkurdistanregionofiraq |