Abstract
Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time series along with external knowledge, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detect robust multi-variate temporal (RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying robust multi-scale temporal features of multi-variate time series.
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- Iyad Batal, Dmitriy Fradkin, James Harrison, Fabian Moerchen, and Milos Hauskrecht. 2012. Mining recent temporal patterns for event detection in multivariate time series data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 280--288. Google Scholar
Digital Library
- Donald J. Bemdt and James Clifford. 1994. Using Dynamic Time Warping to Find Patterns in Time Series.Google Scholar
- David M. Blei and John D. Lafferty. 2006. Dynamic topic models. In Proceedings of the 33rd International Conference on Machine Learning (ICML’06). 113--120.Google Scholar
- K. Selçuk Candan, Rosaria Rossini, Maria Luisa Sapino, and Xiaolan Wang. 2012. sDTW: Computing DTW distances using locally relevant constraints based on salient feature alignments. Proc. VLDB 5, 11 (Jul. 2012), 1519--1530.Google Scholar
- K. Selçuk Candan and Maria Luisa Sapino. 2010. Data Management for Multimedia Retrieval. Cambridge University Press, New York, NY.Google Scholar
- N. Castro and P. Azevedo. 2010. Multiresolution motif discovery in time series. In Proceedings of the SIAM International Conference on Data Mining (SDM’10). SIAM, Columbus, OH, 665--676. Google Scholar
Cross Ref
- Lei Chen. 2005. Similarity Search over Time Series and Trajectory Data. Ph.D. Dissertation. University of Waterloo.Google Scholar
- Lei Chen and Raymond Ng. 2004. On the marriage of Lp-norms and edit distance. In Proceedings of the Thirtieth international conference on Very large data bases (VLDB’04) 30 (2004), 792--803.Google Scholar
Digital Library
- Yanping Chen, Eamonn Keogh, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, and Gustavo Batista. 2015. The UCR Time Series Classification Archive. Retrieved from www.cs.ucr.edu/∼eamonn/time_series_data/.Google Scholar
- F. L. Chung, T. C. Fu, R. Luk, and V. Ng. 2001. Flexible time series pattern matching based on perceptually important points. In Proceedings of the IJCAI Workshop on Learning from Temporal and Spatial Data (2001), 1--7.Google Scholar
- Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, and Eamonn Keogh. 2008. Querying and mining of time series data: Experimental comparison of representations and distance measures. In Proceedings of the VLDB Endowment 1, 2 (2008), 1542--1552. Google Scholar
Digital Library
- Michael Eichler. 2006. Granger causality and path diagrams for multivariate time series. J. Econometr. (2006).Google Scholar
- EmitLab-ASU. 2017. RMT Code. (2017). Available upon request.Google Scholar
- Philippe Esling and Carlos Agon. 2012. Time-series data mining. ACM Comput. Surv. 45, 1 (Dec. 2012), 12:1--12:34.Google Scholar
Digital Library
- C. Harris and M. Stephens. 1988. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference (1988), 147--151.Google Scholar
- A. Harvey and S. Koopman. 1997. Multivariate structural time series model. In System Dynamics in Economic and Financial Models. John Wiley 8 Sons, 269--296.Google Scholar
- A. S. Hopkins, A. Lekov, J. Lutz, G. Rosenquist, and L. Gu. 2011. Simulating a nationally representative housing sample using energyplus. (2011), 55.Google Scholar
- Xiaonan Ji, James Bailey, and Guozhu Dong. 2007. Mining minimal distinguishing subsequence patterns with gap constraints. In Knowledge and Information Systems 11, 3 (2007), 259--286. Google Scholar
Digital Library
- Yohan Jin and Balakrishnan Prabhakaran. 2011. Knowledge discovery from 3D human motion streams through semantic dimensional reduction. ACM Trans. Multimedia Comput. Commun. Appl. 7, 2 (2 2011). DOI:http://dx.doi.org/10.1145/1925101.1925104 Google Scholar
Digital Library
- Eamonn Keogh. 2002. Exact indexing of dynamic time warping. In Proceedings of the 28th International Conference on Very Large Data Bases (VLDB’02). Hong Kong, China, 406--417. Google Scholar
Cross Ref
- Eamonn Keogh and Chotirat Ann Ratanamahatana. 2005. Exact indexing of dynamic time warping. Knowledge and Information Systems 7, 3 (2005), 358--386. Google Scholar
Digital Library
- Duk-Jin Kim and B. Prabhakaran. 2011. Motion fault detection and isolation in body sensor networks. Perv. Mobile Comput. 7, 6 (2011), 727--745. DOI:http://dx.doi.org/10.1016/j.pmcj.2011.09.006 Google Scholar
Digital Library
- Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Rev. 51, 3 (2009), 455--500. Google Scholar
Digital Library
- Joseph B. Kruskal. 1983. An overview of sequence comparison: Time warps, string edits, and macromolecules. SIAM Rev. 25, 2 (1983), 201--237. Google Scholar
Digital Library
- W. Krzanowski. 1979. Between-groups comparison of principal components. Journal of the American Statistical Association 74, 367 (1979), 703--707. Google Scholar
Cross Ref
- Chuanjun Li, S. Q. Zheng, and B. Prabhakaran. 2007. Segmentation and recognition of motion streams by similarity search. ACM Trans. Multimedia Comput. Commun. Appl. 3, 3, Article 16 (Aug. 2007). DOI:http://dx.doi.org/10.1145/1236471.1236475 Google Scholar
Digital Library
- Lei Li, B. Aditya Prakash, and Christos Faloutsos. 2010. Parsimonious linear fingerprinting for time series. Proceedings of the VLDB Endowment 3, 1--2 (2010), 385--396.Google Scholar
Digital Library
- Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. 2003. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD’03). ACM, 2--11. Google Scholar
Digital Library
- Sicong Liu, Yash Garg, K Selcuk Candan, Maria Luisa Sapino, and Gerardo Chowell. 2015. NOTES2: Networks-of-traces for epidemic spread simulations. In Proceedings of the AAAI International Workshop on Computational Sustainability (co-located with AAAI’15).Google Scholar
- D. G. Lowe. 1999. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision (ICCV’99). Google Scholar
Cross Ref
- D. G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2 (2004), 91--110. Google Scholar
Digital Library
- Sachin Mehta, Rajarathnam Nallusamy, Ranjeet Vinayak Marawar, and Balakrishnan Prabhakaran. 2013. A study of DWT and SVD based watermarking algorithms for patient privacy in medical images. In Proceedings of the IEEE International Conference on Healthcare Informatics (ICHI’13). 287--296. DOI:http://dx.doi.org/10.1109/ICHI.2013.41 Google Scholar
Digital Library
- Terence C. Mills. 1990. Time Series Techniques for Economists. Cambridge University Press.Google Scholar
- Mocap. 2001. CMU Mocap data set. Retrieved from http://mocap.cs.cmu.edu/.Google Scholar
- Yasser Mohammad and Toyoaki Nishida. 2009. Constrained motif discovery in time series. New Generat. Comput. 27, 4 (2009), 319--346. Google Scholar
Cross Ref
- Fabian Mörchen. 2003. Time series feature extraction for data mining using DWT and DFT. Philipps-University Marburg.Google Scholar
- Jinglin Peng, Hongzhi Wang, Jianzhong Li, and Hong Gao. 2016. Set-based similarity search for time series. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, Fatma Özcan, Georgia Koutrika, and Sam Madden (Eds.). ACM, 2039--2052. DOI:http://dx.doi.org/10.1145/2882903.2882963 Google Scholar
Digital Library
- C. S. Perng, H. Wang, S. R. Zhang, and D. S. Parker Jr. 2000. Landmarks: A new model for similarity-based pattern querying in time series databases. InProceedings of the IEEE International Conference on Data Engineering (ICDE’00). 33--42. Google Scholar
Cross Ref
- Silvestro Roberto Poccia and Yash Garg. 2017. On the effectiveness of distance measures for similarity search in multi-variate sensory data: Effectiveness of distance measures for similarity search. In Proceedings of the 8th International Conference on Management Research (ICMR’17). 489--493.Google Scholar
- Silvestro Roberto Poccia, Maria Luisa Sapino, Xilun Chen Sicong Liu, Yash Garg, Shengyu Huang, Jung Hyun Kim, Xinsheng Li, Parth Nagarkar, and K. Selçuk Candan. 2017. SIMDMS: Data management and analysis to support decision making through large simulation ensembles. In Proceedings of the International Conference on Extending Database Technology (EDBT’17). 582--585.Google Scholar
- Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. 2012. Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). Google Scholar
Digital Library
- T. Rakthanmanon and E. Keogh. 2013. Fast-shapelets: A scalable algorithm for discovering time series shapelets. In Proceedings of the SIAM International Conference on Data Mining (SDM’13). Google Scholar
Cross Ref
- Hiroaki Sakoe and Seibi Chiba. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26, 1 (1978), 43--49. Google Scholar
Cross Ref
- Gerard Salton and Michael J. McGill. 1983. Introduction to Modern Information Retrieval.Google Scholar
- Parinya Sanguansat. 2012. Multiple multidimensional sequence alignment using generalized dynamic time warping. WSEAS Transactions on Mathematics 11, 8 (2012), 668--678.Google Scholar
- Liang Shuai, Chao Li, Xiaohu Guo, Balakrishnan Prabhakaran, and Jinxiang Chai. 2017. Motion capture with ellipsoidal skeleton using multiple depth cameras. IEEE Trans. Vis. Comput. Graph. 23, 2 (2017), 1085--1098. DOI:http://dx.doi.org/10.1109/TVCG.2016.2520926 Google Scholar
Digital Library
- A. de Silva, R. J. Hyndman, and R. Snyder. 2010. The vector innovations structural time series framework: A simple approach to multivariate forecasting. Statist. Model. 10, 4 (2010), 353--374. Google Scholar
Cross Ref
- Ugo Vespier, Arno Knobbe, Siegfried Nijssen, and Joaquin Vanschoren. 2012. MDL-Based Analysis of Time Series at Multiple Time-Scales. 371--386.Google Scholar
- Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, and Eamonn Keogh. 2006. Indexing multidimensional time-series. VLDB J. 15, 1 (Jan. 2006), 1--20. DOI:http://dx.doi.org/10.1007/s00778-004-0144-2 Google Scholar
Digital Library
- Xinxin Wang and K. Selçuk Candan. 2010. Relevant shape contour snippet extraction with metadata supported hidden markov models. In Proceedings of the Conference on Image and Video Retrieval (CIVR’10). 430--437.Google Scholar
- X. Wang, K. S. Candan, and M. L. Sapino. 2014. Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features. In Proceedings of the 2014 IEEE 30th International Conference on Data Engineering (ICDE’14). IEEE, 388--399.Google Scholar
- Xing Wang, Jessica Lin, Pavel Senin, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, and Susan Frankenstein. 2016. RPM: Representative pattern mining for efficient time series classification. In Proceedings of the 19th International Conference on Extending Database Technology (EDBT’16). 185--196. DOI:http://dx.doi.org/10.5441/002/edbt.2016.19Google Scholar
- Kiyoung Yang and Cyrus Shahabi. 2004. A PCA-based similarity measure for multivariate time series. In Proceedings of the ACM International Workshop on Multimedia Databases (MMDB’04). ACM, 65--74.Google Scholar
Digital Library
- Dragomir Yankov, Eamonn Keogh, Jose Medina, Bill Chiu, and Victor Zordan. 2007. Detecting time series motifs under uniform scaling. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07). ACM, 844--853. Google Scholar
Digital Library
- Lexiang Ye and Eamonn Keogh. 2009. Time series shapelets: A new primitive for data mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). 947--956. Google Scholar
Digital Library
Index Terms
Robust Multi-Variate Temporal Features of Multi-Variate Time Series
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