Tourism Forecast with Weather, Event, and Cross-industry Data

Simone Lionetti, Daniel Pfäffli, Marc Pouly, Tim vor der Brück, Philipp Wegelin

Abstract

The ability to make accurate forecasts on the number of customers is a pre-requisite for efficient planning and use of resources in various industries. It also contributes to global challenges of society such as food waste. Tourism is a domain particularly focussed on short-term forecasting for which the existing literature suggests that calendar and weather data are the most important sources for accurate prediction. We collected and make available a dataset with visitor counts over ten years from four different businesses representative for the tourism sector in Switzerland, along with nearly a thousand features comprising weather, calendar, event and lag information. Evaluation of a plethora of machine learning models revealed that even very advanced deep learning models as well as industry benchmarks show performance at most on a par with simple (piecewise) linear models. Notwithstanding the fact that weather and event features are relevant, contrary to expectations, they proved insufficient for high-quality forecasting. Moreover, and again in contradiction to the existing literature, performance could not be improved by including cross-industry data.

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Paper Citation


in Harvard Style

Lionetti S., Pfäffli D., Pouly M., vor der Brück T. and Wegelin P. (2021). Tourism Forecast with Weather, Event, and Cross-industry Data.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1097-1104. DOI: 10.5220/0010323010971104


in Bibtex Style

@conference{icaart21,
author={Simone Lionetti and Daniel Pfäffli and Marc Pouly and Tim vor der Brück and Philipp Wegelin},
title={Tourism Forecast with Weather, Event, and Cross-industry Data},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1097-1104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010323010971104},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Tourism Forecast with Weather, Event, and Cross-industry Data
SN - 978-989-758-484-8
AU - Lionetti S.
AU - Pfäffli D.
AU - Pouly M.
AU - vor der Brück T.
AU - Wegelin P.
PY - 2021
SP - 1097
EP - 1104
DO - 10.5220/0010323010971104