A Sales Forecast Model for the German Automobile Market Based on Time Series Analysis and Data Mining Methods

Authors:
Bernhard Brühl;Marco Hülsmann;Detlef Borscheid;Christoph M. Friedrich;Dirk Reith
Affiliations:
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany 53754 and Present address: Universität zu Köln, Seminar für ABWL, ...;Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany 53754;BDW Automotive, Leverkusen, Germany 51381;Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany 53754;Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany 53754
Venue:
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Year:
2009

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Abstract

In this contribution, various sales forecast models for the German automobile market are developed and tested. Our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability. Yearly, quarterly and monthly data for newly registered automobiles from 1992 to 2007 serve as the basis for the tests of these models. The time series model used consists of additive components: trend, seasonal, calendar and error component. The three latter components are estimated univariately while the trend component is estimated multivariately by Multiple Linear Regression as well as by a Support Vector Machine. Possible influences which are considered include macro-economic and market-specific factors. These influences are analysed by a feature selection. We found the non-linear model to be superior. Furthermore, the quarterly data provided the most accurate results.