{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:21:50Z","timestamp":1763544110237,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["103-2511-S-845-006-"],"award-info":[{"award-number":["103-2511-S-845-006-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Accurate forecasting of inbound visitor numbers is crucial for effective planning and resource allocation in the tourism industry. Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. Regression models, on the other hand, face challenges when dealing with high-dimensional data. Previous autoencoders for feature selection do not simultaneously incorporate feature and target information simultaneously, potentially limiting their effectiveness in improving predictive performance. This study presents a novel approach that combines a target-concatenated autoencoder (TCA) with ensemble learning to enhance the accuracy of tourism demand predictions. The TCA method integrates the prediction target into the training process, ensuring that the learned feature representations are optimized for specific forecasting tasks. Extensive experiments conducted on the Taiwan and Hawaii datasets demonstrate that the proposed TCA method significantly outperforms traditional feature selection techniques and other advanced algorithms in terms of the mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The results show that TCA combined with XGBoost achieves MAPE values of 3.3947% and 4.0059% for the Taiwan and Hawaii datasets, respectively, indicating substantial improvements over existing methods. Additionally, the proposed approach yields better R2 and MAE metrics than existing methods, further demonstrating its effectiveness. This study highlights the potential of TCA in providing reliable and accurate forecasts, thereby supporting strategic planning, infrastructure development, and sustainable growth in the tourism sector. Future research is advised to explore real-time data integration, expanded feature sets, and hybrid modeling approaches to further enhance the capabilities of the proposed framework.<\/jats:p>","DOI":"10.3390\/make6030083","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T13:10:28Z","timestamp":1721913028000},"page":"1673-1698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8737-7227","authenticated-orcid":false,"given":"Ray-I","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2749-9457","authenticated-orcid":false,"given":"Chih-Yung","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan"},{"name":"Department of Education, University of Taipei, Taipei 100234, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1735-4337","authenticated-orcid":false,"given":"Yu-Wei","family":"Chang","sequence":"additional","affiliation":[{"name":"Doctorate Program on Cybersecurity, Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/forecast4010003","article-title":"Analyzing and forecasting tourism demand in Vietnam with artificial neural networks","volume":"4","author":"Nguyen","year":"2021","journal-title":"Forecasting"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"121388","DOI":"10.1016\/j.eswa.2023.121388","article-title":"Forecasting tourism demand with a novel robust decomposition and ensemble framework","volume":"236","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103675","DOI":"10.1016\/j.annals.2023.103675","article-title":"Forecasting daily tourism demand with multiple factors","volume":"103","author":"Xu","year":"2023","journal-title":"Ann. Tour. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103791","DOI":"10.1016\/j.annals.2024.103791","article-title":"Enhancing tourism demand forecasting with a transformer-based framework","volume":"107","author":"Li","year":"2024","journal-title":"Ann. Tour. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"122930","DOI":"10.1016\/j.eswa.2023.122930","article-title":"EMD-based model with cooperative training mechanism for tourism demand forecasting","volume":"244","author":"Liao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"126663","DOI":"10.1016\/j.neucom.2023.126663","article-title":"A novel model for tourism demand forecasting with spatial\u2013temporal feature enhancement and image-driven method","volume":"556","author":"Dong","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_7","first-page":"67","article-title":"Opportunities and challenges of feature selection methods for high dimensional data: A review","volume":"26","author":"Subbiah","year":"2021","journal-title":"Ing. Syst. d\u2019Information"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s40747-021-00637-x","article-title":"Feature dimensionality reduction: A review","volume":"8","author":"Jia","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"56","DOI":"10.38094\/jastt1224","article-title":"A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction","volume":"1","author":"Zebari","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.ins.2020.12.026","article-title":"Robust distribution-based nonnegative matrix factorizations for dimensionality reduction","volume":"552","author":"Peng","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3428635","article-title":"Material recognition using robotic hand with capacitive tactile sensor array and machine learning","volume":"73","author":"Liu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Feng, G. (2024). Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0303088"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, L., Huynh, D.Q., and Mansour, M.R. (2019, January 22\u201325). Loss switching fusion with similarity search for video classification. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803051"},{"key":"ref_14","unstructured":"Jarrett, D., and van der Schaar, M. (2020). Target-embedding autoencoders for supervised representation learning. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yigit, G.O., and Baransel, C. (2023). A novel autoencoder-based feature selection method for drug-target interaction prediction with human-interpretable feature weights. Symmetry, 15.","DOI":"10.3390\/sym15010192"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5897\/JHMT2019.0276","article-title":"Factors determining international tourist flow to tourism destinations: A systematic review","volume":"12","author":"Gidebo","year":"2021","journal-title":"J. Hosp. Manag. Tour."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1111\/j.1468-5876.2011.00563.x","article-title":"Aggregation, heterogeneous autoregression and volatility of daily international tourist arrivals and exchange rates","volume":"63","author":"Chang","year":"2012","journal-title":"Jpn. Econ. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.5367\/000000010790872015","article-title":"Exchange rate regimes and tourism","volume":"16","year":"2010","journal-title":"Tour. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.econmod.2012.09.050","article-title":"Devaluation, pass-through and foreign reserves dynamics in a tourism economy","volume":"30","author":"Chao","year":"2013","journal-title":"Econ. Model."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/S0261-5177(03)00009-8","article-title":"Incorporating the rough sets theory into travel demand analysis","volume":"24","author":"Goh","year":"2003","journal-title":"Tour. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104208","DOI":"10.1016\/j.tourman.2020.104208","article-title":"Forecasting chinese cruise tourism demand with big data: An optimized machine learning approach","volume":"82","author":"Xie","year":"2021","journal-title":"Tour. Manag."},{"key":"ref_22","first-page":"65","article-title":"A nonlinear dynamic model for international tourism demand on the Spanish Mediterranean coasts","volume":"21","author":"Albaladejo","year":"2018","journal-title":"Econ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/B:EARE.0000035453.78041.71","article-title":"The value of snowfall to skiers and boarders","volume":"29","author":"Englin","year":"2004","journal-title":"Environ. Resour. Econ."},{"key":"ref_24","first-page":"21","article-title":"The influence of a hotel firm\u2019s quality of service and image and its effect on tourism customer loyalty","volume":"12","author":"Kandampully","year":"2011","journal-title":"Int. J. Hosp. Tour. Adm."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5367\/000000003322662989","article-title":"Modelling short-break holiday destination choices","volume":"9","author":"Huybers","year":"2003","journal-title":"Tour. Econ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ongan, S., I\u0219ik, C., and Ozdemir, D. (2017). The effects of real exchange rates and income on international tourism demand for the USA from some European Union countries. Economies, 5.","DOI":"10.3390\/economies5040051"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1177\/0047287516669050","article-title":"Forecasting destination weekly hotel occupancy with big data","volume":"56","author":"Pan","year":"2017","journal-title":"J. Travel Res."},{"key":"ref_28","first-page":"24","article-title":"Impact of weather conditions on tourism demand in the peak summer season over the last 50 years","volume":"9","author":"Falk","year":"2014","journal-title":"Tour. Manag. Perspect."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1177\/0047287512461569","article-title":"Measuring the effect of weather on tourism: A destination and activity-based analysis","volume":"52","author":"Becken","year":"2013","journal-title":"J. Travel Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102923","DOI":"10.1016\/j.annals.2020.102923","article-title":"Daily tourism volume forecasting for tourist attractions","volume":"83","author":"Bi","year":"2020","journal-title":"Ann. Tour. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.5367\/000000010790872079","article-title":"Forecasting British tourist arrivals in the Balearic Islands using meteorological variables","volume":"16","year":"2010","journal-title":"Tour. Econ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.tourman.2014.07.014","article-title":"Can google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach","volume":"46","author":"Skeete","year":"2015","journal-title":"Tour. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.tourman.2014.07.019","article-title":"Forecasting Chinese tourist volume with search engine data","volume":"46","author":"Yang","year":"2015","journal-title":"Tour. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.tourman.2018.03.006","article-title":"Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index","volume":"68","author":"Li","year":"2018","journal-title":"Tour. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, K., Lu, W., Liang, C., and Wang, B. (2019). Intelligence in tourism management: A hybrid FOA-BP method on daily tourism demand forecasting with web search data. Mathematics, 7.","DOI":"10.3390\/math7060531"},{"key":"ref_36","first-page":"196","article-title":"Forecasting hotel room demand using search engine data","volume":"3","author":"Pan","year":"2012","journal-title":"J. Hosp. Tour. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, B., Pu, Y., Wang, Y., and Li, J. (2019). Forecasting hotel accommodation demand based on LSTM model incorporating internet search index. Sustainability, 11.","DOI":"10.3390\/su11174708"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.annals.2019.01.014","article-title":"Tourism demand forecasting: A deep learning approach","volume":"75","author":"Law","year":"2019","journal-title":"Ann. Tour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/for.3980040103","article-title":"Exponential smoothing: The state of the art","volume":"4","author":"Gardner","year":"1985","journal-title":"J. Forecast."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ijforecast.2003.09.015","article-title":"Forecasting seasonals and trends by exponentially weighted moving averages","volume":"20","author":"Holt","year":"2004","journal-title":"Int. J. Forecast."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.enconman.2010.06.053","article-title":"Forecasting energy consumption using a grey model improved by incorporating genetic programming","volume":"52","author":"Lee","year":"2011","journal-title":"Energy Convers. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chang, Y.-W., and Tsai, C.-Y. (2017, January 27\u201329). Apply deep learning neural network to forecast number of tourists. Proceedings of the 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan.","DOI":"10.1109\/WAINA.2017.125"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_44","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_45","first-page":"19667","article-title":"NVAE: A deep hierarchical variational autoencoder","volume":"33","author":"Vahdat","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","first-page":"1","article-title":"Factors affecting tourism industry and its impacts on global economy of the world","volume":"1","author":"Khan","year":"2020","journal-title":"SSRN Electron. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sapkota, P., Vashisth, K.K., and Ranabhat, D. (2023). A systematic literature review on factors affecting rural tourism. International Conference on Intelligent Computing & Optimization, Springer.","DOI":"10.1007\/978-3-031-50158-6_5"},{"key":"ref_48","first-page":"06005","article-title":"Tourism industry resilience and its influencing factors: An experience from 60 countries","volume":"409","author":"Luo","year":"2023","journal-title":"E3S Web Conf."},{"key":"ref_49","unstructured":"De Rainville, F.-M., Fortin, F.-A., Gardner, M.-A., Parizeau, M., and Gagne, C. (2012, January 7\u201311). DEAP: A python framework for evolutionary algorithms. Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, Philadelphia, PA, USA."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/83\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:23:12Z","timestamp":1760109792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/6\/3\/83"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["make6030083"],"URL":"https:\/\/doi.org\/10.3390\/make6030083","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2024,7,25]]}}}