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General sales forecast models for automobile markets and their analysis

: Hülsmann, Marco; Borscheid, Detlef; Friedrich, Christoph M.; Reith, Dirk

Preprint urn:nbn:de:0011-n-1993483 (2.0 MByte PDF)
MD5 Fingerprint: 4243e654a3418cf6232a267ae887a0e1
Created on: 27.3.2012

Transactions on machine learning and data mining 5 (2012), No.2, pp.65-86
ISSN: 1865-6781
Journal Article, Electronic Publication
Fraunhofer SCAI ()
sales forecast; time series analysis; data mining; automobile industry; decision tree

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 eects 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-specic 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 consider ed 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.