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Price forecasting for used construction equipment is a challenging task dueto spatial and temporal price fluctuations. It is thus of high interest toautomate the forecasting process based on current market data. Even thoughapplying machine learning (ML) to these data represents a promising approach topredict the residual value of certain tools, it is hard to implement for smalland medium-sized enterprises due to their insufficient ML expertise. To thisend, we demonstrate the possibility of substituting manually created MLpipelines with automated machine learning (AutoML) solutions, whichautomatically generate the underlying pipelines. We combine AutoML methods withthe domain knowledge of the companies. Based on the CRISP-DM process, we splitthe manual ML pipeline into a machine learning and non-machine learning part.To take all complex industrial requirements into account and to demonstrate theapplicability of our new approach, we designed a novel metric named methodevaluation score, which incorporates the most important technical andnon-technical metrics for quality and usability. Based on this metric, we showin a case study for the industrial use case of price forecasting, that domainknowledge combined with AutoML can weaken the dependence on ML experts forinnovative small and medium-sized enterprises which are interested inconducting such solutions.

Benchmarking Automated Machine Learning Methods for Price Forecasting Applications