Abstract
Time series forecasting is often done “one step ahead” with statistical models or numerical machine learning algorithms. It is possible to extend those predictive models to a few steps ahead and iterate the predictions thus allowing further forecasting. However, it is not possible to do this for thousands of data points because cumulative error tends to make the long term forecasting unreliable. Such uncertainty can be conveied by the use of fuzzy forecasting where the forecasted value is a fuzzy set rather than a number. The end-user can only appreciate the uncertainty of the forecast if the forecasting model is easy to understand. Contrary to common “black-box” models, we use symbolic machine learning on symbolic representations of time-series. In this paper, we tackle the real-world issue of forecasting electric load for one year, sampled every ten minutes, with data available for the past few years. In this context, future values are not only related to their short term previous values, but also to temporal attributes (the day of the week, holidays ...). We use a symbolic machine learning algorithm (decision tree) to extract this kind of knowledge and predict future pattern occurences. Those patterns are learnt when building a symbolic representation of the time series, by clustering episodes showing similar patterns and making the cluster a symbolic attribute of the episodes. Intra-class variations result in forecasting uncertainty that we model through fuzzy prototypes. Those prototypes are then used to construct a fuzzy forecasting easily understood by the end-user.
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
Lotfi A. Zadeh. Fuzzy sets. Information and Control, 8:338–353, 1965.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and regression trees. Technical report, Wadsworth International, Monterey, CA, 1984.
C.-C. Chang and C.-J. Lin. Ijcnn 2001 challenge: Generalization ability and text decoding. In IJCNN, 2001.
C. Holt. Forecasting seasonals and trends by exponentially weighted moving averages. Technical report, ONR Research Memorandum, Carnegie Institute, 1957.
Selina Chu, Eamonn Keogh, David Hart, and Michael Pazzani. Iterative deepening dynamic time warpping. In Second SIAM International Conference on Data Mining, April 2002.
C. L. Giles, S. Lawrence, and A. C. Tsoi. Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning, 44(1–2):161–183, July/August 2001 2001.
Georges Hebrail and Bernard Hugueney. Symbolic representation of long time-series. In Workshop of the 4th European Conf. Principles and Practice of Knowledge Discovery in Databases, PKDD, 2000.
Bernard Hugueney. “Représentations symboliques de longues séeries temporelles”. PhD thesis, LIP6, 2003.
Bernard Hugueney and Bernadette Bouchon-Meunier. Time-series segmentation and symbolic representation, from process-monitoring to data-mining. In B. Reusch, editor, Computational Intelligence, Theory and Applications, number 2206 in Lecture Notes in Computer Science, LNCS, pages 118–123. Springer-Verlag, 2001.
Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. Locally adaptive dimensionality reduction for indexing large time series databases. SIGMOD Record (ACM Special Interest Group on Management of Data), 30(2):151–162, June 2001.
Eamonn Keogh and Michael J. Pazanni. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy, editors, Proceedings of the Forth International Conference on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, 1998.
Eamonn J. Keogh, Jessica Lin, and Wagner Truppe. “clustering of time series subsequences is meaningless: Implications for past and future research”. In ICDM, pages 115–122. IEEE Computer Society, 2003.
Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pages 2–11. ACM Press, 2003.
T. Y. Lin. Data mining: Granular computing approach. Lecture Notes in Computer Science, 1574:24–33, 1999.
David Meyer. Naive time series forecasting methods. R News, 2(2):7–10, June 2002.
Klaus-Robert Muller, Alex J. Smola, Gunnar Ratsch, Bernhard Scholkopf, Jens Kohlmorgen, and Vladimir Vapnik. Predicting time series with support vector machines. In ICANN, pages 999–1004, 1997.
P. Winters. Forecasting sales by exponentially weighted moving averages. Management Science, 6(7):324–342, 1960.
Byoung-Kee Yi and Christos Faloutsos. Fast time sequence indexing for arbitrary L p norms. In Amr El Abbadi, Michael L. Brodie, Sharma Chakravarthy, Umeshwar Dayal, Nabil Kamel, Gunter Schlageter, and Kyu-Young Whang, editors, VLDB 2000, Proceedings of 26th International Conference on Very Large Data Bases, September 10–14, 2000, Cairo, Egypt, pages 385–394, Los Altos, CA 94022, USA, 2000. Morgan Kaufmann Publishers.
Byoung-Kee Yi, H. V. Jagadish, and Christos Faloutsos. Efficient retrieval of similar time sequences under time warping. In 14th International Conference on Data Engineering (ICDE’98), pages 201–208, February 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hugueney, B., Bouchon-Meunier, B., Hébrail, G. (2005). Fuzzy Long Term Forecasting through Machine Learning and Symbolic Representations of Time Series. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_10
Download citation
DOI: https://doi.org/10.1007/3-540-31182-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22807-3
Online ISBN: 978-3-540-31182-9
eBook Packages: EngineeringEngineering (R0)