Future Work

Our work uses angles as the representation of shape or trend of time series and the similarity retrieval based on this representation is discussed. There is an extended research on applying EPLR to other data mining tasks such as anomaly detection and motif discovery. As for EPLR, how many data points in a segment is highly data dependent. We may analyze the data distribution to decide the number of data points in a segment. Furthermore, only the trends but not the real values of data are concerned in the work. It may be possible to combine the real values with angles to make the similarity retrieval more robust and powerful.

Bibliography

[1]. R. Agrawal, C. Faloutsos, and A. R. Swami, Efficient Similarity Search Databases, Proceedings of the 4^th International Conf. Foundations of Data Organization and Algorithms (FODO), pp. 69-87, 1993.

[2]. R. Agrawal and R.Srikant, Mining Sequential Patterns, Proc. of 11^th IEEE Intel.

Conf. on Data Engineering (ICDE), pp. 3-14, Mar. 1995

[3]. D.J. Berndt and J.Cliford, Using Dynamic Time Warping to Find Patterns in Time Series. AAAI-94 Workshop on Knowledge Discovery in Databases, pp.350-370, 1994.

[4]. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, Fast Subsequence Matching in Time-Series Database, Proc. of the ACM SIGMOD Intel. Conf. on Management of Data, pp. 419-429, 1994.

[5]. G. Das, D. Gunopulos, and H.Mannila, Finding Similar Time Series, Proc. of Principles of Data Mining and Knowledge Discovery, 1^st (PKDD), Pages 88-100, 1997.

[6]. K-P. Chan and A. Fu, Efficient Time Series Matching by Wavelets, Proc. of the 15^th IEEE Intel. Conf. on Data Engineering (ICDE), pp. 126-133, 1999.

[7]. B.-K. Yi, H.V. Jagadish, and C.Faloutsos, Efficient Retrieval of Similar Time Sequences under Time Warping, Proc. of the 14^th IEEE Intel. Conf. on Data Engineering (ICDE), pp. 201-208, 1998.

[8]. K-P. Chan, A. Fu, and C. Yu, Haar Wavelets for Efficient Similarity Search of Time-series: With and Without Time Warping, Journal of Transactions on

the 21^st Intel. Conf. on Very Large Databases (VLDB), pp. 490-501, 1995.

[10]. J. Gehrke, F. Kor, and D. Srivastava, On computing Correlated Aggregates over Continual Data Streams, Proc. of the ACM SIGMOD Intel. Conf. on Management of Data, pp. 126-133, 2001.

[11]. P. Sanghyun, W. Chu, J. Yoon, and C. Hsu, Efficient Similarity Searches for Time-Warped Subsequences in Sequence Databases, Proc. of the 16^th IEEE Intel. Conf. on Data Engineering (ICDE), pp. 23-32.

[12]. E. J. Keogh, K. Chakrabarti, S. Mehrotra, and M. J. Pazzani, Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp. 151-162, 2001.

[13]. E. J. Keogh, K. Chakrabarti, M. J. Pazzani, and S. Mehrotra, Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases, Journal of Knowledge and Information Systems, 3(3): 263-286, 2001.

[14]. H. Wu, B. Salzberg, and D. Zhang, Online Event-driven Subsequence Matching over Financial Data Streams, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp. 23-34, 2004.

[15]. H. Wu, B. Salzberg, G. C. Sharp, S. B. Jiang, H. Shirato, and D. Kaeli, Subsequence Matching on Structured Time Series Data, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp. 682-693, 2005.

[16]. L. Chen and R.Ng, On the Marriage of Lp-norms and Edit Distance, Proc. of the 30^th Intel. Conf. on Very Large Databases (VLDB), pp. 792-803, 2004.

[17]. L. Chen, M. T. Ozsu, and V. Oria, Robust and Efficient Similarity search for moving object trajectories, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp. 491-502, 2005.

[18]. M. Vlachos, G. Kollios, and D. Gunopulos, Discovering Similar

Engineering (ICDE), pp.673-684, 2002.

[19]. F. Korn, H. Jagadish, and C. Faloutsos, Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp.289-300, 1997.

[20]. E. J. Keogh, S.Chu, D.Hart, and M.J. Pazzani, An Online Algorithm for Segmenting Time Series, Proc. of IEEE Intel. Conf. on Data Mining (ICDM), pp.

289-296, 2001.

[21]. J. Lin, E.J. Keogh, L. Wei, and S. Lonardi, Experiencing SAX: A Novel Symbolic Representation of Time Series, Journal of Data Mining and Knowledge Discovery, 15(2): 107-144, 2007

[22]. E. J. Keogh, C. A. Ratanamahatana, Exact Indexing of Dynamic Time Warping, Journal of Knowledge and Information Systems, 7(3): pp. 358-386, 2005.

[23]. M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. J. Keogh, Indexing Multi-Dimensional Time-series with Support for Multiple Distance Measures, Proc. of the 9^th ACM SIGKDD Intel. Conf. on Knowledge Discovery and Data Mining, pp. 216-225, 2003.

[24]. S.-W. Kim, S. Park, and W. W. Chu, An Index-based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases, Proc. of the 17^th IEEE Intel. Conf. on Data Engineering (ICDE), pp.607-614, 2001.

[25]. Y. Sakurai, M. Yoshikawa, and C. Faloutsos, FTW: Fast Similarity Search under the Time Warping Distance, Proc. of the 24^th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS), pp. 326-337, 2005.

[26]. N. Q. V. Hung and D. T. Anh, Combing SAX and Piecewise Linear

ESAX for Financial Applications, Proc. of the 22^nd Intel. Conf. on Data Engineering Workshops (ICDEW), pp.115, 2006.

[28]. Y. Zhu and D. Shasha, Warping Indexes with Envelope Transforms for Query by Humming, Proc. of ACM SIGMOD Intel. Conf. on Management of Data, pp.181-192, 2003.

[29]. X. Lian and L. Chen, Efficient Similarity Search over Future Stream Time Series, Journal of IEEE Transactions on Knowledge and Data Engineering, 20(1): pp.

40-54, 2008.

[30]. Y. Zhu and D. Shasha, StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time, Proc. of the 28^th Intel. Conf. on Very Large Databases (VLDB), pp: 358-369, 2002.

[31]. E. J. Keogh and S. Kasetty, On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration, Journal of Data Mining and Knowledge Discovery, 7(4): pp. 349-371, 2003.

[32]. C.A. Ratanamahatana and E. J. Keogh, Three Myths about Dynamic Time Warping Data Mining, Proc. of SIAM Intel. Conf. on Data Mining (SDM), pp.506-510, 2005.

[33]. S. Chu, E. J. Keogh, D. Hart, and M. Pazzani, Iterative Deepening Dynamic Time Warping for Time Series, 2^nd SLAM Intel. Conf. on Data Mining, 2002.

[34]. E. J. Keogh and T. Folias. The UCR Time Series Data Mining Archive[http://www.cs.ucr.edu/~eamonn/  time_series_data/], Riverside CA.

University of California – Computer Science and Engineering Department, 2002.

[35]. M. Gavrilov, D. Anguelov, P. Indyk, and R. Motwani, Mining the Stock Market:

Which Measure is Best, Proc. of the 6^th ACM SIGKDD Intel. Conf. on Knowledge Discovery and Data Mining, pp.487-496, 2000.

Document Clustering, Proc. of the 5^th ACM SIGKDD Intel. Conf. on Knowledge Discovery and Data Mining, pp.16-22, 1999.

[37]. E. J. Keogh and M. Pazzani, An Enhanced Representation of Time Series which Allows Fast and Accurate Classification Clustering and Relevance Feedback, Proc. of the 4^th Intel. Conf. on Knowledge Discovery and Data Mining, pp.

239-241, 1998.