Tourist Arrival Forecasting Using Multiscale Mode Learning Model
The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival fore...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/16/2999 |
| _version_ | 1827599137645789184 |
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| author | Kaijian He Don Wu Yingchao Zou |
| author_facet | Kaijian He Don Wu Yingchao Zou |
| author_sort | Kaijian He |
| collection | DOAJ |
| description | The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival forecasting model is proposed to exploit different multiscale data features in tourist arrival movement. Two popular Mode Decomposition models (MD) and the Convolutional Neural Network (CNN) model are introduced to model the multiscale data features in the tourist arrival data The data patterns at different scales are extracted using these two different MD models which dynamically decompose tourist arrival into the distinctive intrinsic mode function (IMF) data components. The convolutional neural network uses the deep network to further model the multiscale data structure of tourist arrivals, with the reduced dimensionality of key multiscale data features and finer modeling of nonlinearity in tourist arrival. Our empirical results using daily tourist arrival data show that the MD-CNN tourist arrival forecasting model significantly improves the forecasting reliability and accuracy. |
| first_indexed | 2024-03-09T04:07:25Z |
| format | Article |
| id | doaj.art-13054bdfb30648ae9f531444f43d0fd6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T04:07:25Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-13054bdfb30648ae9f531444f43d0fd62023-12-03T14:03:50ZengMDPI AGMathematics2227-73902022-08-011016299910.3390/math10162999Tourist Arrival Forecasting Using Multiscale Mode Learning ModelKaijian He0Don Wu1Yingchao Zou2College of Tourism, Hunan Normal University, Changsha 410081, ChinaSchool of Tourism Management, Macao Institute of Tourism Studies, Macao, ChinaCollege of Tourism, Hunan Normal University, Changsha 410081, ChinaThe forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival forecasting model is proposed to exploit different multiscale data features in tourist arrival movement. Two popular Mode Decomposition models (MD) and the Convolutional Neural Network (CNN) model are introduced to model the multiscale data features in the tourist arrival data The data patterns at different scales are extracted using these two different MD models which dynamically decompose tourist arrival into the distinctive intrinsic mode function (IMF) data components. The convolutional neural network uses the deep network to further model the multiscale data structure of tourist arrivals, with the reduced dimensionality of key multiscale data features and finer modeling of nonlinearity in tourist arrival. Our empirical results using daily tourist arrival data show that the MD-CNN tourist arrival forecasting model significantly improves the forecasting reliability and accuracy.https://www.mdpi.com/2227-7390/10/16/2999tourist arrival forecastvariational mode decompositionempirical mode decompositionmultiscale analysisdeep learning modelconvolutional neural network model |
| spellingShingle | Kaijian He Don Wu Yingchao Zou Tourist Arrival Forecasting Using Multiscale Mode Learning Model Mathematics tourist arrival forecast variational mode decomposition empirical mode decomposition multiscale analysis deep learning model convolutional neural network model |
| title | Tourist Arrival Forecasting Using Multiscale Mode Learning Model |
| title_full | Tourist Arrival Forecasting Using Multiscale Mode Learning Model |
| title_fullStr | Tourist Arrival Forecasting Using Multiscale Mode Learning Model |
| title_full_unstemmed | Tourist Arrival Forecasting Using Multiscale Mode Learning Model |
| title_short | Tourist Arrival Forecasting Using Multiscale Mode Learning Model |
| title_sort | tourist arrival forecasting using multiscale mode learning model |
| topic | tourist arrival forecast variational mode decomposition empirical mode decomposition multiscale analysis deep learning model convolutional neural network model |
| url | https://www.mdpi.com/2227-7390/10/16/2999 |
| work_keys_str_mv | AT kaijianhe touristarrivalforecastingusingmultiscalemodelearningmodel AT donwu touristarrivalforecastingusingmultiscalemodelearningmodel AT yingchaozou touristarrivalforecastingusingmultiscalemodelearningmodel |