Abstract
The classification of business cycles is a hard and important problem. Government as well as business decisions rely on the assessment of the current business cycle. In this paper, we investigate how economists can be better supported by a combination of machine learning techniques. We have successfully applied Inductive Logic Programming (ILP). For establishing time and value intervals different discretization procedures are discussed. The rule sets learned from different experiments were analyzed with respect to correlations in order to find a concept drift or shift.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large data bases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’ 94), pages 478–499, Santiago, Chile, sep 1994.
J. F. Allen. Towards a general theory of action and time. Artificial Intelligence, 23:123–154, 1984.
Marlene Amstad. Konjunkturelle Wendepunkte: Datierung und Prognose. St.Gallen, 2000.
Donald J. Berndt and James Clifford. Finding patterns in time series: A dynamic programming approach. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 3, pages 229–248. AAAI Press/The MIT Press, Menlo Park, California, 1996.
Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, and Padhraic Smyth. Rule Discovery from Time Series. In Rakesh Agrawal, Paul E. Stolorz, and Gregory Piatetsky-Shapiro, editors, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pages 16–22, Ney York City, 1998. AAAI Press.
Rheinisch-Westfälisches Institut für Wirtschaftsforschung. Arbeitsbericht 2000. Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Essen, Germany, 2000.
U. Heilemann and H. J. Münch. West German Business Cycles 1963-1994: A Multivariate Discriminant Analysis. CIRET-Conference in Singapore, CIRET-Studien 50, 1996.
U. Heilemann and H. J. Münch. Classification of German Business Cycles Using Monthly Data. SFB-475 Technical Reports 8/2001. Universitaet Dortmund, 2001.
Frank Höppner. Learning temporal rules from state sequences. In Miroslav Kubat and Katharina Morik, editors, Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, pages 25–31, Menlo Park, CA, USA, 2001. IJCAI, AAAI Press. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI).
Eamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. An online algorithm for segmenting time series. In Nick Cercone, T. Y. Lin, and Xindong Wu, editors, Proceedings of the 2001 IEEE International Conference on Data Mining, pages 289–296, San Jose, California, 2001. IEEE Computer Society.
Jörg-Uwe Kietz and Stefan Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. Arbeitspapiere der GMD 503, GMD, mar 1991.
Katharina Morik. The representation race—preprocessing for handling time phenomena. In Ramon López de Mántaras and Enric Plaza, editors, Proceedings of the European Conference on Machine Learning 2000 (ECML 2000), volume 1810 of Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, New York, 2000. Springer Verlag Berlin.
Ursula Sondhauss and Claus Weihs. Incorporating background knowledge for better prediction of cycle phases. Technical Report 24, Universität Dortmund, 2001.
Winfried Theis and Claus Weihs. Clustering techniques for the detection of business cycles. SFB475 Technical Report 40, Universität Dortmund, 1999.
Claus Weihs Ursula Sondhauβ. Using labeled and unlabeled data to learn drifting concepts. In Miroslav Kubat and Katharina Morik, editors, Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, pages 38–44, Menlo Park, CA, USA, 2001. IJCAI, AAAI Press. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI).
Ian Witten and Eibe Frank. Data Mining // Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufmann, 2000.
D. A. Zighed, S. Rabaseda, R. Rakotomalala, and Feschet F. Discretization methods in supervised learning. In Encyclopedia of Computer Science and Technology, volume 40, pages 35–50. Marcel Dekker Inc., 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Morik, K., Rüping, S. (2002). A Multistrategy Approach to the Classification of Phases in Business Cycles. In: Elomaa, T., Mannila, H., Toivonen, H. (eds) Machine Learning: ECML 2002. ECML 2002. Lecture Notes in Computer Science(), vol 2430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36755-1_26
Download citation
DOI: https://doi.org/10.1007/3-540-36755-1_26
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44036-9
Online ISBN: 978-3-540-36755-0
eBook Packages: Springer Book Archive