Transactions on Machine Learning and Data Mining (ISSN: 1865-6781)

Volume 5 - Number 2 - October 2012 - Pages 65-86

General Sales Forecast Models for Automobile Markets and their Analysis

M. Hülsmann1, D. Borscheid2, C. M. Friedrich 1,3, and D. Reith1

1Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
2 BDW Automotive, Maybachstr. 35, 51381 Leverkusen, Germany
3 University of Applied Science and Arts Dortmund, Department of Computer Science, Emil-Figge-Str. 42 /B.2.02, 44227 Dortmund, Germany


In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.

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