Authors:
Simone Lionetti
1
;
Daniel Pfäffli
1
;
Marc Pouly
1
;
Tim vor der Brück
1
and
Philipp Wegelin
2
Affiliations:
1
School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts, Suurstoffi 1, 6343 Rotkreuz, Switzerland
;
2
School of Business, Lucerne University of Applied Sciences and Arts, Zentralstr. 9, 6002 Lucerne, Switzerland
Keyword(s):
Forecasting, Tourism, Machine Learning, Deep Learning, Feature Importance, Dataset.
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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