Abstract
A long standing challenge for time series analysis is to develop representation techniques for dimension reduction while still preserving their fundamental features. As an effective representation technique, Symbolic Aggregate Approximation (SAX) has been widely used for dimension reduction in time series analysis. However, SAX always maps time series data into symbols by definite breakpoints. As a result, the similar points close to the breakpoints cannot be well represented, and thus lead to poor Tightness of Lower Bounds (TLB). To fill this crucial void, in this paper, we develop a time series representation method, named Random Shifting based SAX (rSAX), which has the ability in significantly improving the TLB of representations without increasing the corresponding granularity of representations. Specifically, the key idea of rSAX is to generate a group of breakpoints by random shifting rather than definite breakpoints. Therefore, the points close to each other will have higher probabilities to be mapped into the same symbols, while the points far away from each other will have higher probabilities to be mapped into different symbols. In addition, we also theoretically prove that rSAX can achieve better mapping performances and TLB than SAX. Finally, extensive experiments on several real-world data sets clearly validate the effectiveness and efficiency of the rSAX approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems 47(2), 115–125
Yan, H., Pham, T.: Spectral similarity for analysis of dna microarray time-series data. International Journal of Data Mining and Bioinformatics 1(2), 150–161 (2006)
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2011)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
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 (2008)
Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding motifs in time series. In: The 2nd Workshop on Temporal Data Mining (July 2002)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, vol. 23. ACM (1994)
Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: ICDE 1999. IEEE (1999)
Keogh, E., Lin, J., Fu, A.: Hot sax: Efficiently finding the most unusual time series subsequence. In: ICDM 2005 (2005)
Xi, X., Keogh, E., Wei, L., Mafra-Neto, A.: Finding motifs in a database of shapes. In: SDM 2007 (2007)
Kasetty, S., Stafford, C., Walker, G.P., Wang, X., Keogh, E.: Real-time classification of streaming sensor data. In: ICTAI 2008 (2008)
Camerra, A., Palpanas, T., Shieh, J., Keogh, E.: isax 2.0: Indexing and mining one billion time series. In: ICDM 2010 (2010)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263–286 (2001)
Huang, Y.W., Yu, P.S.: Adaptive query processing for time-series data. In: KDD 1999 (1999)
Megalooikonomou, V., Wang, Q., Li, G., Faloutsos, C.: A multiresolution symbolic representation of time series. In: ICDE 2005 (2005)
Wei, L., Keogh, E., Xi, X.: Saxually explicit images: Finding unusual shapes. In: ICDM 2006 (2006)
Shieh, J., Keogh, E.: isax: indexing and mining terabyte sized time series. In: KDD 2008 (2008)
Spencer, J.: The probabilistic method. In: Proceedings of the Third Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 41–47 (1992)
Keogh, E., Xi, X., Wei, L., Ratanamahatana, C.A.: The ucr time series classification/clustering homepage (2011)
Liao, Y., Wang, K., Zhao, F., Bai, S.: Modern agro-climatic zoning of Hunan Province. Hunan University Press, Changsha (2010)
The climate reports of hunan province, http://www.hnqx.gov.cn
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bai, X., Xiong, Y., Zhu, Y., Zhu, H. (2013). Time Series Representation: A Random Shifting Perspective. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-38562-9_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38561-2
Online ISBN: 978-3-642-38562-9
eBook Packages: Computer ScienceComputer Science (R0)