Abstract
The repeated patterns in a long time series are called as time series motifs. As the motifs can reveal much useful information, time series motif discovery has been received extensive attentions in recent years. Time series motif discovery is an important operation for time series analysis in many fields, such as financial data analysis, medical and health monitoring. Although many algorithms have been proposed for motifs discovery, most of existing works are running on single node and focusing on finding fixed-length motifs. They cannot process very long time series efficiently. However, the length of motifs cannot be predicted previously, and the Euclidean distance has many drawbacks as the similarity measure. In this work, we propose a parallel algorithm based on subsequences correlation called as PMDSC (Parallel Motif Discovery based on Subsequences Correlation), which can be applied to find time series motifs with variable lengths. We have conducted extensive experiments on public data sets, the results demonstrate that our method can efficiently find variable-length motifs in long time series.
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
References
Bugenhagen, S.M., Cowley Jr., A.W., Beard, D.A.: Identifying physiological origins of baroreflex dysfunction in salt-sensitive hypertension in the Dahl SS rat. Physiol. Genomics 42, 23–41 (2010)
Castro, N., Azevedo, P.J.: Multiresolution motif discovery in time series. In: SIAM, pp. 665–676 (2010)
Gao, Y., Lin, J.: Efficient discovery of variable-length time series motifs with large length range in million scale time series. CoRR abs/1802.04883 (2018)
Gao, Y., Lin, J., Rangwala, H.: Iterative grammar-based framework for discovering variable-length time series motifs. In: ICMLA, pp. 7–12 (2016)
Li, Y., U, L.H., Yiu, M.L., Gong, Z.: Quick-motif: an efficient and scalable framework for exact motif discovery. In: ICDE. pp. 579–590 (2015)
Lin, J., Keogh, E., Li, W., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107–144 (2007). https://doi.org/10.1007/s10618-007-0064-z
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of 2nd Workshop on Temporal Data Mining at KDD, pp. 53–68 (2002)
Mueen, A., Hamooni, H., Estrada, T.: Time series join on subsequence correlation. In: ICDM, pp. 450–459 (2014)
Mueen, A.: Enumeration of time series motifs of all lengths. In: ICDM, pp. 547–556 (2013)
Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, M.B.: Exact discovery of time series motifs. In: SIAM, pp. 473–484 (2009)
Nunthanid, P., Niennattrakul, V., Ratanamahatana, C.A.: Discovery of variable length time series motif. In: EEE, pp. 472–475 (2011)
Rebbapragada, U., Protopapas, P., Brodley, C.E., Alcock, C.: Finding anomalous periodic time series. Mach. Learn. 74, 281–313 (2009). https://doi.org/10.1007/s10994-008-5093-3
Rong, C., Chen, L., Silva, Y.N.: Parallel time series join using spark. Concurr. Comput. Pract. Exp. 32(9), e5622 (2020)
Senin, P., et al.: GrammarViz 2.0: a tool for grammar-based pattern discovery in time series. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD. LNCS, vol. 8726, pp. 468–472. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44845-8_37
Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of time-series motif from multi-dimensional data based on MDL principle. Mach. Learn. 58, 269–300 (2005). https://doi.org/10.1007/s10994-005-5829-2
Yeh, C.C.M., Yan, Z., Ulanova, L., Begum, N., Keogh, E.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: ICDM, pp. 1317–1322 (2016)
Zhu, Y., Zimmerman, Z., Senobari, N.S., et al.: Matrix profile II: exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In: ICDM, pp. 739–748 (2016)
Acknowledgment
This work was supported by the project of Natural Science Foundation of China (No. 61402329, No. 61972456), the Natural Science Foundation of Tianjin (No. 19JCYBJC15400) and Natural Science Foundation of Tianjin-Science and Technology Correspondent Project (No. 18JCTPJC63300).
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
Rong, C., Chen, L., Lin, C., Yuan, C. (2020). Parallel Variable-Length Motif Discovery in Time Series Using Subsequences Correlation. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-60290-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60289-5
Online ISBN: 978-3-030-60290-1
eBook Packages: Computer ScienceComputer Science (R0)