Abstract
Time series classification is an important problem in data mining with several applications in different domains. Because time series data are usually high dimensional, dimensionality reduction techniques have been proposed as an efficient approach to lower their dimensionality. One of the most popular dimensionality reduction techniques of time series data is the Symbolic Aggregate Approximation (SAX), which is inspired by algorithms from text mining and bioinformatics. SAX is simple and efficient because it uses precomputed distances. The disadvantage of SAX is its inability to accurately represent important points in the time series. In this paper we present Extreme-SAX (E-SAX), which uses only the extreme points of each segment to represent the time series. E-SAX has exactly the same simplicity and efficiency of the original SAX, yet it gives better results in time series classification than the original SAX, as we show in extensive experiments on a variety of time series datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31, 606–660 (2017)
Baydogan, M., Runger, G., Tuv, E.: A bag-of-features framework to classify time series. IEEE Trans. Pattern Anal. Mach. Intell. 25(11), 2796–2802 (2013)
Bramer, M.: Principles of Data Mining. Springer, London (2007). https://doi.org/10.1007/978-1-84628-766-4
Chen, Y., et al.: The UCR time series classification archive (2015). www.cs.ucr.edu/~eamonn/time_series_data
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series. In: Proceedings of the 34th VLDB (2008)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Adversarial attacks on deep neural networks for time series classification. In: Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July (2019)
Hatami, N., Gavet, Y., Debayle, J.: Bag of recurrence patterns representation for time-series classification. Pattern Anal. Appl. 22, 877–887 (2019)
Karim, F., Majumdar, S., Darabi, H., Chen, S.: LSTM fully convolutional networks for time series classification. IEEE Access 1–7 (2017)
Keogh, E., Chakrabarti, K., Pazzani, M. and Mehrotra: Dimensionality reduction for fast similarity search in large time series databases. J. Know. Inform. Sys. (2000)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for similarity search in large time series databases. In: SIGMOD, pp. 151–162 (2001)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.Y.: A symbolic representation of time series, with implications for streaming algorithms. DMKD 2003, 2–11 (2003)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2) (2007)
Lkhagava, B., Suzuki, Y., Kawagoe, K.: Extended SAX: extension of symbolic aggregate approximation for financial time series data representation. In: Proceedings of the Data Engineering Workshop 2006, 2006, 4A0-8 (2006)
Muhammad Fuad, M.M.: Differential evolution versus genetic algorithms: towards symbolic aggregate approximation of non-normalized time series. Sixteenth International Database Engineering & Applications Symposium– IDEAS’12, Prague, Czech Republic,8–10 August, 2012. Published by BytePress/ACM (2012)
Muhammad Fuad, M.M.: Genetic algorithms-based symbolic aggregate approximation. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 105–116. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32584-7_9
Ratanamahatana, C., Keogh, E.: Making time-series classification more accurate using learned constraints. In: Proceedings of the SIAM International Conference on Data Mining, pp. 11–22 (2004)
Ratanamahatana, C., Keogh, E., Bagnall, A.J., Lonardi, S.: A novel bit level time series representation with implication of similarity search and clustering. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 771–777. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_90
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. In: Proceedings of the International Joint Conference on Neural Networking (IJCNN), May 2017, pp. 1578–1585 (2017)
Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of the 26th International Conference on Very Large Databases, Cairo, Egypt (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Muhammad Fuad, M.M. (2020). Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification. In: Song, M., Song, IY., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science(), vol 12393. Springer, Cham. https://doi.org/10.1007/978-3-030-59065-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-59065-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59064-2
Online ISBN: 978-3-030-59065-9
eBook Packages: Computer ScienceComputer Science (R0)