Deep Machine Learning for Time Series Inbound Tourism Forecasting

Abstract: Accurate inbound tourism flow forecasting has been a challenge for all stakeholders related to the sector. The multidisciplinary character of the tourism product which has been directly and indirectly influenced by all types of risks, cataclysms and crises further exposed its intangible nature to shocks and flows disruption. Thus, forecasting inbound tourism flows with advanced data science and AI (artificial intelligence) methods has been gaining momen­tum, which the COVID-19 pandemic boosted. Therefore, this paper aims to ex­amine the relevant AI forecasting methods by applying a deep machine learn­ing technique comparing different Python time series forecasting libraries via a Jupyter Notebook computer environment. Bulgaria’s inbound tourism data has been used to develop an advanced deep neural network with the DARTS Python library and compare its accuracy with other Python library models.

7th International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2023 – Conference Proceedings, Hybrid (Faculty of Organization and Informatics Varaždin, University of Zagreb, Croatia), October 26, 2023

ITEMA Conference Proceedings published by: Association of Economists and Managers of the Balkans – Belgrade, Serbia

ITEMA conference partners: Faculty of Economics and Business, University of Maribor, Slovenia; Faculty of Organization and Informatics, University of Zagreb, Varaždin; Faculty of Geography, University of Belgrade, Serbia; Institute of Marketing, Poznan University of Economics and Business, Poland; Faculty of Agriculture, Banat’s University of Agricultural Sciences and Veterinary Medicine ”King Michael I of Romania”, Romania

ITEMA Conference 2023 Conference Proceedings: ISBN 978-86-80194-75-2, ISSN 2683-5991, DOI: https://doi.org/10.31410/ITEMA.2023

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission.

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