Abstract
Efficient algorithms of time series data mining have the common denominator of utilizing the special time structure of the attributes of time series. To accommodate the information of time dimension into the process, we propose a novel instance-level cursor based indexing technique, which is combined with a decision tree algorithm. This is beneficial for several reasons: (a) it is insensitive to the time level noise (for example rendering, time shifting), (b) its working method can be interpreted, making the explanation of the classification process more understandable, and (c) it can manage time series of different length. The implemented algorithm named ShiftTree is compared to the well-known instance-based time series classifier 1-NN using different distance metrics, used over all 20 datasets of a public benchmark time series database and two more public time series datasets. On these benchmark datasets, our experiments show that the new model-based algorithm has an average accuracy slightly better than the most efficient instance-based methods, and there are multiple datasets where our model-based classifier exceeds the accuracy of instance-based methods. We also evaluated our algorithm via blind testing on the 20 datasets of the SIGKDD 2007 Time Series Classification Challenge. To improve the model accuracy and to avoid model overfitting, we provide forest methods as well.
Chapter PDF
Similar content being viewed by others
References
Abou-Nasr, M., Feldkamp, L.: Ford Classification Challange (2008), http://home.comcast.net/~nn_classification/
Acir, N.: Classification of ecg beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm. Neural Computing and Applications 14(4), 299–309 (2005)
Azoff, E.M.: Neural Network Time Series: Forecasting of Financial Markets. Wiley, Chichester (1994)
Pong Chan, K., Chee Fu, A.W.: Efficient time series matching by wavelets. In: ICDE (1999)
Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: VLDB (2004)
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD Conference (2005)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: Experimental comparison of representations and distance measures. In: VLDB (2008)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: SIGMOD Conference Proceedings (1994)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences (55), 119–139 (1997)
Jager, H.: The echo state approach to analysing and training recurrent neural networks. GMD Report 148 8, 1–42 (August 2001)
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3) (2005)
Keogh, E., Shelton, C., Moerchen, F.: Workshop and Challenge on Time Series Classification at SIGKDD 2007 (2007), http://www.cs.ucr.edu/~eamonn/SIGKDD2007TimeSeries.html
Keogh, E., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering Homepage (2006), http://www.cs.ucr.edu/~eamonn/time_series_data/
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: 2nd Workshop on Temporal Data Mining (KDD 2002), pp. 53–68 (2002)
Prekopcsak, Z.: Accelerometer based real-time gesture recognition. In: POSTER 2008: Proceedings of the 12th International Student Conference on Electrical Engineering (May 2008)
Quinlan, J.R.: Induction of decision trees. In: Readings in Machine Learning. Morgan Kaufmann, San Francisco (1990)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. Data Engineering (August 2002)
Vlachos, M., Gunopulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: ICDE (2002)
Zhu, J., Rosset, S., Zou, H., Hastie, T.: Multi-class adaboost. Statistics and Its Interface 2, 349–360 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hidasi, B., Gáspár-Papanek, C. (2011). ShiftTree: An Interpretable Model-Based Approach for Time Series Classification. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23783-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-23783-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23782-9
Online ISBN: 978-3-642-23783-6
eBook Packages: Computer ScienceComputer Science (R0)