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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lewandowski, R.: Prognose- und Informationssysteme und ihre Anwendungen. de Gruyter, Berlin (1974)
Lewandowski, R.: Prognose- und Informationssysteme und ihre Anwendungen Band II. de Gruyter, Berlin (1980)
Dudenhöffer, F.: Prognosemethoden für den PKW-Markt: Das Beispiel Dieselfahrzeuge. In: WISU-Wirtschaftsstudium, pp. 1092–1100 (2002)
Dudenhöffer, F., Borscheid, D.: Automobilmarkt-Prognosen: Modelle und Methoden. In: Automotive Management. Strategie und Marketing in der Automobilwirtschaft, pp. 192–202 (2004)
Bäck, T., Hammel, U., Lewandowski, R., Mandischer, M., Naujoks, B., Rolf, S., Schütz, M., Schwefel, H.-P., Sprave, J., Theis, S.: Evolutionary Algorithms: Applications at the Informatik Center Dortmund. In: Genetic Algorithms in Engineering and Computer Science, pp. 175–204 (1997)
Statistisches Landesamt des Freistaates Sachsen, http://www.statistik.sachsen.de/21/14_01/14_01_definitionen.pdf (last accessed Feburary 2009)
Hüttner, M.: Markt- und Absatzprognosen. Kohlhammer, Stuttgart (1982)
Stier, W.: Methoden der Zeitreihenanalyse. Springer, Heidelberg (2001)
Stier, W.: Verfahren zur Analyse saisonaler Schwankungen in ökonomischen Zeitreihen. Springer, Heidelberg (1980)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis forecasting and control. Holden-Day, San Francisco (1976)
Leiner, B.: Einführung in die Zeitreihenanalyse. R. Oldenbourg Verlag, München - Wien (1982)
Kessler, W.: Multivariate Datenanalyse. Wiley-VHC (2007)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Christianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based methods. Cambridge University Press, Cambridge (2000)
Chen, K., Wang, C.: Support vector regression with genetic algorithms in forecasting tourism demand. In: Tourism Management, pp. 1–13 (2006)
Trafalis, T.B., Ince, H.: Support Vector Machine for Regression and Applications to Financial Forecasting. In: International joint conference on neutral networks, vol. 6, pp. 348–353 (2000)
Yale School of Public Health, http://publichealth.yale.edu/faculty/labs/guan/Papers%20under%20review/KPSS.pdf (last accessed Feburary 2009)
Dunteman, G.H.: Principal Component Analysis. Sage Publications, Thousand Oaks (1989)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence Journal, Special Issue on Relevance, 273–324 (1997)
Witten, I.H., Frank, E.: Data Mining. Morgan Kaufmann Publishers, San Francisco (2005)
Brühl, B.: Absatzprognosen für die Automobilindustrie in der Bundesrepublik Deutschland. Diploma Thesis, University of Cologne (2008)
Taleb, N.N.: The Fourth Quadrant: A Map of the Limits of Statistics. Edge 257 (2008), http://www.edge.org (last accessed April 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Brühl, B., Hülsmann, M., Borscheid, D., Friedrich, C.M., Reith, D. (2009). A Sales Forecast Model for the German Automobile Market Based on Time Series Analysis and Data Mining Methods. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-03067-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03066-6
Online ISBN: 978-3-642-03067-3
eBook Packages: Computer ScienceComputer Science (R0)