A Soft Voting Ensemble Model for Hotel Revenue Prediction

Yuxin Jiang; Chengjie Ni; Mingjing Chen

doi:doi:10.11648/j.ijefm.20241205.13

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| Peer-Reviewed

A Soft Voting Ensemble Model for Hotel Revenue Prediction

Yuxin Jiang

, Chengjie Ni^*

, Mingjing Chen

Published in International Journal of Economics, Finance and Management Sciences (Volume 12, Issue 5)

Received: 9 August 2024 Accepted: 9 September 2024 Published: 11 September 2024

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Abstract

In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 12, Issue 5)
DOI	10.11648/j.ijefm.20241205.13
Page(s)	258-266
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Soft Voting, Ensemble Model, Hotel Revenue, Prediction

References

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[12]	Lin, W., Lin, Q. Z., Feng, L., & Tan, K. C. (2024). Ensemble of domain adaptation-based knowledge transfer for evolutionary multitasking. IEEE Transactions on Evolutionary Computation, 28(2), 388-402. https://doi.org/10.1109/TEVC.2023.3259067
[13]	Taherkhani, L., Daneshvar, A., Khalili, H. A., Sanaei, M. R. (2024). Intelligent decision support system using nested ensemble approach for customer churn in the hotel industry. Journal of Business Analytics, 7(2), 83-93. https://doi.org/10.1080/2573234X.2023.2281317
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APA Style

Jiang, Y., Ni, C., Chen, M. (2024). A Soft Voting Ensemble Model for Hotel Revenue Prediction. International Journal of Economics, Finance and Management Sciences, 12(5), 258-266. https://doi.org/10.11648/j.ijefm.20241205.13

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ACS Style

Jiang, Y.; Ni, C.; Chen, M. A Soft Voting Ensemble Model for Hotel Revenue Prediction. Int. J. Econ. Finance Manag. Sci. 2024, 12(5), 258-266. doi: 10.11648/j.ijefm.20241205.13

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AMA Style

Jiang Y, Ni C, Chen M. A Soft Voting Ensemble Model for Hotel Revenue Prediction. Int J Econ Finance Manag Sci. 2024;12(5):258-266. doi: 10.11648/j.ijefm.20241205.13

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@article{10.11648/j.ijefm.20241205.13,
  author = {Yuxin Jiang and Chengjie Ni and Mingjing Chen},
  title = {A Soft Voting Ensemble Model for Hotel Revenue Prediction
},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {12},
  number = {5},
  pages = {258-266},
  doi = {10.11648/j.ijefm.20241205.13},
  url = {https://doi.org/10.11648/j.ijefm.20241205.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20241205.13},
  abstract = {In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.
},
 year = {2024}
}

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TY  - JOUR
T1  - A Soft Voting Ensemble Model for Hotel Revenue Prediction

AU  - Yuxin Jiang
AU  - Chengjie Ni
AU  - Mingjing Chen
Y1  - 2024/09/11
PY  - 2024
N1  - https://doi.org/10.11648/j.ijefm.20241205.13
DO  - 10.11648/j.ijefm.20241205.13
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 258
EP  - 266
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20241205.13
AB  - In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

VL  - 12
IS  - 5
ER  -

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Author Information

Yuxin Jiang

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0009-0008-2316-6771
Chengjie Ni

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0009-0000-9298-960X
Mingjing Chen

School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

Contact Email

http://orcid.org/0009-0001-8469-4235

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Plain Text BibTeX RIS

APA Style

Jiang, Y., Ni, C., Chen, M. (2024). A Soft Voting Ensemble Model for Hotel Revenue Prediction. International Journal of Economics, Finance and Management Sciences, 12(5), 258-266. https://doi.org/10.11648/j.ijefm.20241205.13

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ACS Style

Jiang, Y.; Ni, C.; Chen, M. A Soft Voting Ensemble Model for Hotel Revenue Prediction. Int. J. Econ. Finance Manag. Sci. 2024, 12(5), 258-266. doi: 10.11648/j.ijefm.20241205.13

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AMA Style

Jiang Y, Ni C, Chen M. A Soft Voting Ensemble Model for Hotel Revenue Prediction. Int J Econ Finance Manag Sci. 2024;12(5):258-266. doi: 10.11648/j.ijefm.20241205.13

Copy | Download

@article{10.11648/j.ijefm.20241205.13,
  author = {Yuxin Jiang and Chengjie Ni and Mingjing Chen},
  title = {A Soft Voting Ensemble Model for Hotel Revenue Prediction
},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {12},
  number = {5},
  pages = {258-266},
  doi = {10.11648/j.ijefm.20241205.13},
  url = {https://doi.org/10.11648/j.ijefm.20241205.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20241205.13},
  abstract = {In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Soft Voting Ensemble Model for Hotel Revenue Prediction

AU  - Yuxin Jiang
AU  - Chengjie Ni
AU  - Mingjing Chen
Y1  - 2024/09/11
PY  - 2024
N1  - https://doi.org/10.11648/j.ijefm.20241205.13
DO  - 10.11648/j.ijefm.20241205.13
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 258
EP  - 266
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20241205.13
AB  - In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

VL  - 12
IS  - 5
ER  -

Copy | Download