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  4. Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
 
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2023
Conference Paper
Title

Benchmarking Automated Machine Learning Methods for Price Forecasting Applications

Abstract
Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting process based on current market data. Even though applying machine learning (ML) to these data represents a promising approach to predict the residual value of certain tools, it is hard to implement for small and medium-sized enterprises due to their insufficient ML expertise. To this end, we demonstrate the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions, which automatically generate the underlying pipelines. We combine AutoML methods with the domain knowledge of the companies. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. To take all complex industrial requirements into account and to demonstrate the applicability of our new approach, we designed a novel metric named method eval uation score, which incorporates the most important technical and non-technical metrics for quality and usability. Based on this metric, we show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts for innovative small and medium-sized enterprises which are interested in conducting such solutions.
Author(s)
Stühler, Horst
Zeppelin GmbH
Zöller, Marc-André
2USU Software AG
Klau, Dennis
Univ. Stuttgart, Institut für Arbeitswissenschaft und Technologiemanagement -IAT-  
Beiderwellen-Bedrikow, Alexandre
Zeppelin GmbH
Tutschku, Christian Klaus
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Mainwork
DATA 2023, 12th International Conference on Data Science, Technology and Applications. Proceedings  
Project(s)
Quantencomputing - Neue Potenziale für automatisiertes Machine Learning
Funder
Bundesministerium für Wirtschaft und Klimaschutz  
Conference
International Conference on Data Science, Technology and Applications 2023  
Open Access
DOI
10.5220/0012051400003541
10.24406/publica-2446
File(s)
2023_Klau_Benchmarking Automated Machine Learning Methods.pdf (561.18 KB)
Rights
CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives
Language
English
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Keyword(s)
  • Construction Equipment

  • Price Forecasting

  • Machine Learning

  • ML

  • AutoML

  • CRISP-DM

  • Case Study

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