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End-to-End Implementation of Automated Price Forecasting Applications

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Abstract

Forecasting prices of used construction equipment is challenging due to spatial and temporal price fluctuations. Automating this forecasting process using current market data is, therefore, highly desirable. A promising and common strategy is the application of machine learning (ML) techniques. However, small and medium-sized enterprise often struggle with the implementation of ML approaches due to a lack of ML expertise. In response, we demonstrate the potential of substituting manually created ML pipelines with automated machine learning (AutoML) solutions, which autonomously create the underlying pipelines. Therefore, we follow the CRISP-DM process to identify tasks requiring ML expertise. First, we dissect the ML pipeline into an machine learning and non-machine learning part and use AutoML to automate the former. Consecutively, we also automate the data preprocessing step, being part of the non-machine learning tasks, to further reduce the dependency on data processing expertise. Additionally, we implement a data-centric result evaluation, rating the reliability of the trained ML models. This approach supports the domain-driven creation of ML pipelines, democratizing the use of ML. To address all complex industrial requirements and showcase the practicality of our approach, we developed an innovative metric called method evaluation score. This metric encompasses key technical and non-technical parameters essential for domain experts to assess the quality and usability of the generated models. Based on this metric, we demonstrate in our case study that combining domain knowledge with AutoML and automatic preprocessing can reduce the reliance on ML experts for innovative small and medium-sized enterprise keen on adopting such technologies.

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Data availability

The data is available within the GitHub repository depicted in https://github.com/AutoQML/End-to-End-Automated-Price-Forecasting.

Notes

  1. The market portals are https://www.mascus.de, https://catused.cat.com, https://www.mobile.de, https://machineryline.de, https://trademachines.de, https://www.truck1.eu, and https://www.truckscout24.de.

  2. See https://github.com/AutoQML/End-to-End-Automated-Price-Forecasting.

  3. The list of hyperparameters is available in Appendix 7.

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Acknowledgements

This work was partly funded by the German Federal Ministry of Economic Affairs and Climate Action in the research project AutoQML (Grant no. 01MQ22002).

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Authors and Affiliations

  1. Zeppelin GmbH, Graf-Zeppelin-Platz 1, 85748, Garching, Germany

    Horst Stühler & Alexandre Beiderwellen-Bedrikow

  2. Fraunhofer IAO, Nobelstraße 12, 70569, Stuttgart, Germany

    Dennis Klau & Christian Tutschku

  3. USU GmbH, Rüppurrer Str. 1, 76137, Karlsruhe, Germany

    Marc-André Zöller

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  1. Horst Stühler
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Correspondence to Horst Stühler.

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This article is part of the topical collection “Recent Trends on Data Science, Technology and Applications” guest edited by Slimane Hammoudi, Alfredo Cuzzocrea and Oleg Gusikhin.

Appendices

Appendix A Example Usage

The manual implementation of the ML methods (Polynomial Regression, Decision Tree, Random Forest, Support Vector Regressor, K-Nearest Neighbor, AdaBoost Regressor and Multy Layer Perceptron) require approximately 50 lines of code (LOC) on average and approximately 13 different libraries:

figure a

On the other hand training and prediction with AutoGluon can be implemented within three lines of code:

figure b

The same holds for AutoSklearn

figure c

Flaml

figure d

and AutoKeras

figure e

Appendix B Search Spaces

See Tables 4, 5, 6.

Table 4 ML and AutoML methods
Full size table
Table 5 AutoML configuration
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Table 6 ML Hyperparameter
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Stühler, H., Klau, D., Zöller, MA. et al. End-to-End Implementation of Automated Price Forecasting Applications. SN COMPUT. SCI. 5, 402 (2024). https://doi.org/10.1007/s42979-024-02735-2

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