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General sales forecast models for automobile markets based on time series analysis and data mining techniques

: Hülsmann, M.; Borscheid, D.; Friedrich, C.M.; Reith, D.


Perner, P.:
Advances in data mining. Proceedings : Applications and theoretical aspects. 11th industrial conference, ICDM 2011, New York, NY, USA, August 30 - September 3, 2011
Berlin: Springer, 2011 (Lecture Notes in Artificial Intelligence 6870)
ISBN: 978-3-642-23183-4
ISBN: 978-3-642-23184-1
ISSN: 0302-9743
Industrial Conference on Data Mining (ICDM) <11, 2011, New York/NY>
Fraunhofer SCAI ()

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.