Tourism Demand Forecasting Based on Grey Model and BP Neural Network
This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5528383 |
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| _version_ | 1832554596911808512 |
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| author | Xing Ma |
| author_facet | Xing Ma |
| author_sort | Xing Ma |
| collection | DOAJ |
| description | This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability. |
| format | Article |
| id | doaj-art-d24bc83c57e54054aff93ea119e048ae |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-d24bc83c57e54054aff93ea119e048ae2025-02-03T05:51:12ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55283835528383Tourism Demand Forecasting Based on Grey Model and BP Neural NetworkXing Ma0College of Land and Tourism, Luoyang Normal University, Luoyang 471934, ChinaThis article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.http://dx.doi.org/10.1155/2021/5528383 |
| spellingShingle | Xing Ma Tourism Demand Forecasting Based on Grey Model and BP Neural Network Complexity |
| title | Tourism Demand Forecasting Based on Grey Model and BP Neural Network |
| title_full | Tourism Demand Forecasting Based on Grey Model and BP Neural Network |
| title_fullStr | Tourism Demand Forecasting Based on Grey Model and BP Neural Network |
| title_full_unstemmed | Tourism Demand Forecasting Based on Grey Model and BP Neural Network |
| title_short | Tourism Demand Forecasting Based on Grey Model and BP Neural Network |
| title_sort | tourism demand forecasting based on grey model and bp neural network |
| url | http://dx.doi.org/10.1155/2021/5528383 |
| work_keys_str_mv | AT xingma tourismdemandforecastingbasedongreymodelandbpneuralnetwork |