Benchmarking Automated Machine Learning Methods for Price Forecasting Applications

TL;DR

This paper explores automating machine learning pipelines for price forecasting in construction equipment, combining AutoML with domain knowledge to make advanced analytics accessible to smaller enterprises.

Contribution

It introduces a novel approach integrating AutoML with domain expertise and proposes a new evaluation metric tailored for industrial price forecasting applications.

Findings

AutoML can effectively automate ML pipeline creation for price forecasting.

Combining domain knowledge with AutoML reduces dependence on ML experts.

The proposed method improves usability and quality in industrial price prediction tasks.

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 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.