Abstract
In the last decade symbolic representations approaches have shown effectiveness for knowledge discovery in time series, such as the Symbolic Aggregate Approximation (SAX). However, SAX doesn’t preserve the local slope information of the time series because it uses only the mean values of the segments. The modification Extended SAX (ESAX) proposed to treat this problem by the dimensionality increase. In this paper, we present a symbolic representation method that preserves the behavior of local slope characteristics in the symbolic representations of the time series. The proposed method was evaluated with three different discretization approaches and compared with the SAX and the ESAX algorithms. The experimental evaluation, using artificial and real datasets with 1-nearest-neighbor classification, demonstrate the method effectiveness to reduce the error rates of time series classification and to keep the local slope information in the symbolic representations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Antunes, C.M., Oliveira, A.L.: Temporal Data Mining – An Overview. In: Workshop on Temporal Data Mining at the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), pp. 1–15 (2001)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series, with Implications for Atreaming Algorithms. In: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2003), pp. 2–11. ACM (2003)
Karamitopoulos, L., Evagelidis, G.: Current Trends in Time Series Representation. In: 11th Panhellenic Conference on Informatics (PCI 2007), pp. 217–226 (2007)
Laxman, S., Sastry, P.S.: A Survey of Temporal Data Mining. Sadhana 31(2), 173–198 (2006)
Fu, T.C.: A Review on Time Series Data Mining. Engineering Applications of Artificial Intelligence 24(1), 164–181 (2010)
Hugueney, B.: Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 545–552. Springer, Heidelberg (2006)
Pham, N.D., Le, Q.L., Dang, T.K.: Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases. In: 12th International Asia-Pacific Web Conference (APWEB 2010), pp. 181–187. IEEE (2010)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and Mining of Time Series Data – Experimental Comparison of Representations and Distance Measures. VLDB Endowment 1(2), 1542–1552 (2008)
Batyrshin, I., Sheremetov, L.: Perception-Based Approach to Time Series Data Mining. Applied Soft Computing 8(3), 1211–1221 (2008)
Lkhagva, B., Suzuki, Y., Kawagoe, K.: New Time Series Representation ESAX for Financial Applications. In: 22nd International Conference on Data Engineering Workshops (ICDEW 2006), pp. 17–22. IEEE (2006)
Zalewski, W., Silva, F., Lee, H.D., Maletzke, A.G., Wu, F.C.: A Symbolic Representation Method to Preserve the Characteristic Slope of Time Series. In: 21st Brazilian Symposium on Artificial Intelligence, SBIA 2012 (to appear, 2012)
Alonso, F., Martinez, L., Perez-Perez, A., Santamaria, A., Valente, J.P.: Modelling Medical Time Series Using Grammar-Guided Genetic Programming. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 32–46. Springer, Heidelberg (2008)
Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering Homepage (2011), www.cs.ucr.edu/~eamonn/time_series_data/
Gorecki, T., Luczak, M.: Using Derivatives in Time Series Classification. In: Data Mining and Knowledge Discovery, pp. 1–22 (in press, 2012)
Batista, G.E.A.P.A., Wang, X., Keogh, E.J.: A Complexity-Invariant Distance Measure for Time Series. In: 11th SIAM International Conference on Data Mining (SDM 2011), pp. 699–710 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zalewski, W., Silva, F., Lee, H.D., Maletzke, A.G., Wu, F.C. (2012). Time Series Discretization Based on the Approximation of the Local Slope Information. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-34654-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34653-8
Online ISBN: 978-3-642-34654-5
eBook Packages: Computer ScienceComputer Science (R0)