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 evaluation 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.

翻译：二手施工设备的价格预测由于空间和时间的价格波动是一项具有挑战性的任务。因此，基于当前市场数据自动化预测过程具有高度价值。尽管将机器学习应用于这些数据是预测特定工具残值的一种有前景的方法，但由于中小型企业缺乏足够的机器学习专业知识，其实施较为困难。为此，我们展示了用自动化机器学习（AutoML）解决方案替代人工构建的机器学习管道的可能性，该方案能自动生成底层管道。我们将AutoML方法与企业的领域知识相结合。基于CRISP-DM流程，我们将人工机器学习管道分为机器学习部分和非机器学习部分。为满足所有复杂的工业需求并证明新方法的适用性，我们设计了一种名为方法评估分数的新指标，该指标整合了质量和可用性方面最重要的技术与非技术指标。基于该指标，我们在价格预测这一工业用例的案例研究中表明，领域知识与AutoML的结合可以削弱对机器学习专家的依赖，从而惠及有意实施此类解决方案的创新性中小型企业。