{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:55:39Z","timestamp":1774454139283,"version":"3.50.1"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"Introduction<\/jats:title>Tourism planning, particularly in rural areas, presents complex challenges due to the highly dynamic and interdependent nature of tourism demand, influenced by seasonal, geographical, and economic factors. Traditional tourism forecasting methods, such as ARIMA and Prophet, often rely on statistical models that are limited in their ability to capture long-term dependencies and multi-dimensional data interactions. These methods struggle with sparse and irregular data commonly found in rural tourism datasets, leading to less accurate predictions and suboptimal decision-making.<\/jats:p><\/jats:sec>Methods<\/jats:title>To address these issues, we propose NeuroTourism xLSTM, a neuro-inspired model designed to handle the unique complexities of rural tourism planning. Our model integrates an extended Long Short-Term Memory (xLSTM) framework with spatial and temporal attention mechanisms and a memory module, enabling it to capture both short-term fluctuations and long-term trends in tourism data. Additionally, the model employs a multi-objective optimization framework to balance competing goals such as revenue maximization, environmental sustainability, and socio-economic development.<\/jats:p><\/jats:sec>Results<\/jats:title>Experimental results on four diverse datasets, including ETT, M4, Weather2K, and the Tourism Forecasting Competition datasets, demonstrate that NeuroTourism xLSTM significantly outperforms traditional methods in terms of accuracy.<\/jats:p><\/jats:sec>Discussion<\/jats:title>The model's ability to process complex data dependencies and deliver precise predictions makes it a valuable tool for rural tourism planners, offering actionable insights that can enhance strategic decision-making and resource allocation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fncom.2025.1495313","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T06:06:19Z","timestamp":1744092379000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["TourismNeuro xLSTM: neuro-inspired xLSTM for rural tourism planning and innovation"],"prefix":"10.3389","volume":"19","author":[{"given":"Jing","family":"Jiang","sequence":"first","affiliation":[]},{"given":"You","family":"Li","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"B1","volume-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"key":"B2","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ijforecast.2006.03.005","article-title":"Exponential smoothing: The state of the artpart II","volume":"22","author":"Gardner Jr","year":"2006","journal-title":"Int. J. Forecast"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1177\/1354816618812588","article-title":"Advances in tourism demand forecasting: a review of operational, theoretical and economic factors","author":"Goh","year":"2019","journal-title":"Tour. Econ"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comp"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1338104","DOI":"10.3389\/fnbot.2023.1338104","article-title":"Education robot object detection with a brain-inspired approach integrating faster R-CNN, yolov3, and semi-supervised learning","volume":"17","author":"Hong","year":"2024","journal-title":"Front. Neurorobot"},{"key":"B6","unstructured":"\u201cForecasting principles and practice,\u201d\n \n \n \n Hyndman\n R. J.\n \n \n Athanasopoulos\n G.\n \n \n OTexts\n \n 2021"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1451924","DOI":"10.3389\/fnbot.2024.1451924","article-title":"Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix","volume":"18","author":"Li","year":"2024","journal-title":"Front. Neurorobot"},{"key":"B8","doi-asserted-by":"publisher","first-page":"136143","DOI":"10.1145\/3426826.3426837","article-title":"The role of machine learning in tourism crisis management: a case study of the covid-19 pandemic","volume":"46","author":"Li","year":"2020","journal-title":"J. Hosp. Tour. Managem"},{"key":"B9","doi-asserted-by":"publisher","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":"B10","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int. J. Forecast"},{"key":"B11","doi-asserted-by":"publisher","first-page":"103311","DOI":"10.1007\/s00521-024-09659-1","article-title":"Spatio-temporal deep learning models for tourism demand forecasting: a survey","volume":"91","author":"Liu","year":"2022","journal-title":"Ann. Tour. Res"},{"key":"B12","unstructured":"Tourism demand forecasting using deep learning techniques: a review\n \n \n \n Liu\n Z.\n \n \n Zhang\n X.\n \n \n Zhang\n Z.\n \n \n J. Hosp. Tour. Technol\n \n 2022"},{"key":"B13","unstructured":"\u201cN-beats: Neural basis expansion analysis for interpretable time series forecasting,\u201d\n \n \n \n Oreshkin\n B. N.\n \n \n Carpov\n D.\n \n \n Chapados\n N.\n \n \n Bengio\n Y.\n \n \n International Conference on Learning Representations\n \n 2019"},{"key":"B14","first-page":"17","article-title":"The impact of tourist arrivals on hotel occupancy rate: Evidence from malaysia","volume":"72","author":"Song","year":"2019","journal-title":"Tour. Managem"},{"key":"B15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Prophet: forecasting at scale","volume":"72","author":"Taylor","year":"2020","journal-title":"Am. Statist"},{"key":"B16","unstructured":"\u201cAttention is all you need,\u201d\n \n \n \n Vaswani\n A.\n \n \n Shazeer\n N.\n \n \n Parmar\n N.\n \n \n Uszkoreit\n J.\n \n \n Jones\n L.\n \n \n Gomez\n A. N.\n \n \n Kaiser\n \u0141.\n \n \n Polosukhin\n I.\n \n \n Advances in Neural Information Processing Systems\n \n 2017"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1361577","DOI":"10.3389\/fnbot.2024.1361577","article-title":"Machine unlearning in brain-inspired neural network paradigms","volume":"18","author":"Wang","year":"2024","journal-title":"Front. Neurorobot"},{"key":"B18","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s00500-021-06695-0","article-title":"Improving tourism demand forecasting performance: A meta-learning approach","volume":"24","author":"Wang","year":"2018","journal-title":"Tour. Econ"},{"key":"B19","first-page":"105","article-title":"Deep learning in tourism demand forecasting: a hybrid spatio-temporal framework for improving prediction accuracy","volume":"51","author":"Wang","year":"2022","journal-title":"J. Hosp. Tour. Managem"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1382406","DOI":"10.3389\/fnbot.2024.1382406","article-title":"Brain-inspired semantic data augmentation for multi-style images","volume":"18","author":"Wang","year":"2024","journal-title":"Front. Neurorobot"},{"key":"B21","doi-asserted-by":"publisher","first-page":"036030","DOI":"10.1117\/1.JRS.10.036030","article-title":"Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration","volume":"10","author":"Wang","year":"2016","journal-title":"J. Appl. Remote Sens"},{"key":"B22","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jpdc.2019.03.003","article-title":"The security of machine learning in an adversarial setting: A survey","volume":"130","author":"Wang","year":"","journal-title":"Journal of Parallel and Distributed Computing"},{"key":"B23","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.ins.2019.07.023","article-title":"Multilevel similarity model for high-resolution remote sensing image registration","volume":"505","author":"Wang","year":"","journal-title":"Inform. Sci"},{"key":"B24","unstructured":"\u201cSpatial temporal graph convolutional networks for skeleton-based action recognition,\u201d\n \n \n \n Yan\n S.\n \n \n Xiong\n Y.\n \n \n Lin\n D.\n \n \n Proceedings of the AAAI Conference on Artificial Intelligence\n \n 2018"},{"key":"B25","unstructured":"\u201cSpatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,\u201d\n \n \n \n Yu\n B.\n \n \n Yin\n H.\n \n \n Zhu\n Z.\n \n \n Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)\n \n 2018"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13042-024-02157-9","article-title":"Daily tourism demand forecasting and tourists search behavior analysis: a deep learning approach","volume":"2024","author":"Zhang","year":"2024","journal-title":"Int. J. Mach. Learn. Cybernet"},{"key":"B27","unstructured":"Tourism demand forecasting based on hybrid model using machine learning and statistical methods\n \n 104265\n \n \n Zhou\n J.\n \n \n Xu\n Y.\n \n \n Wang\n L.\n \n \n Tour. Managem\n 84\n 2021"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1375886","DOI":"10.3389\/fnbot.2024.1375886","article-title":"Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced yolov4 framework","volume":"18","author":"Zhu","year":"2024","journal-title":"Front. Neurorobot"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2025.1495313\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:51:22Z","timestamp":1744206682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2025.1495313\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"references-count":28,"alternative-id":["10.3389\/fncom.2025.1495313"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2025.1495313","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]},"article-number":"1495313"}}