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  2. Customer forecast with cross-industry data Customer forecast with cross-industry data

Customer forecast with cross-industry data

In the leisure/tourism market, customer numbers fluctuate short-term, making forecasts difficult. With a large amount of data from different companies, we want to improve forecast quality by taking advantage of correlation effects.

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

01.10.2019 - 31.01.2020

Overview

In the leisure, tourism and mobility markets, customer volumes fluctuate short-term. It is difficult for SMEs in this sector to estimate customer volumes with sufficient precision and to react appropriately to fluctuations. The result can be a misallocation of resources. As part of the Innosuisse project "Short-term forecasting and control of the number of guests in the leisure and tourism market", econometric models and methods of machine learning were evaluated in order to predict the number of visitors for SMEs in the leisure, tourism and mobility sectors. The project has shown that the data of a single company are not enough to predict the fluctuation with sufficient accuracy.

In this project, we examine the hypothesis that a more informative dataset can be created through the merger of several company data in the leisure/tourism and mobility sector due to the correlation effects. We want to show that this new database can significantly increase the quality of forecasting for the individual company.
In addition to linear econometric models, we use machine learning methods such as support vector machines and neural networks to predict the customer volume on a daily/ hourly basis.
Historical data from nine companies in the Lucerne area for the period 2007 - 2018 are available for this project.

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Facts

Type of project

Forschung

Internal organisations involved
  • Computer Science and Information Technology
Funding
  • Private / Stiftungen
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Persons involved: internal

Project manager
  • Daniel Pfäffli
Project Co-Head
  • Marc Pouly
Member of project team
  • Marc Bravin
  • Andrin Bürli
  • Donnacha Daly
  • Simone Lionetti
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Persons involved: external

External project manager
  • Philipp Wegelin

Publications

  • Article, review; peer reviewed (1)

    • Lionetti, Simone; Pfäffli, Daniel; Pouly, Marc; vor der Brück, Tim & Wegelin, Philipp (2021). Tourism Forecast with Weather, Event, and Cross-Industry Data. Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 1097-1104. doi: 10.5220/0010323010971104

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

10/01/2019 - 01/31/2020

Project Head

Daniel Pfäffli

Highly Specialised Senior Research Associate

+41 41 757 68 28

Show email

Project Co-Head

Prof. Dr. Marc Pouly

Lecturer

+41 41 757 68 25

Show email

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+41 41 228 42 42

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