- 1.Agrawal, R., Lin, K. I., Sawhney, H. S., & Shim, K. (1995). Fast similarity search in the presence of noise, scaling, and translation in times-series databases. In VLDB, September.]] Google ScholarDigital Library
- 2.Bay, S. (1999). UCI Repository of Kdd databases Department of Information and Computer Science]]Google Scholar
- 3.Berndt, D. & Clifford, J. (1994) Using dynamic time warping to find patterns in time series. AAAI-94 Workshop on Knowledge Discovery in Databases. Seattle, Washington.]]Google Scholar
- 4.Caiani, E.G., Porta, A., Baselli, G., Turiel, M., Muzzupappa, S., Pieruzzi, F., Crema, C., Malliani, A. & Cerutti, S. (1998) Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology. Vol. 25 Cat.]]Google Scholar
- 5.Das, G., Lin, K., Mannila, H., Renganathan, G. & Smyth, P. (1998). Rule discovery form time series. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 16-22, AAAI Press.]]Google Scholar
- 6.Debregeas, A. & Hebrail, G. (1998). Interactive interpretation of Kohonen maps applied to curves. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 179-183, AAAI Press.]]Google Scholar
- 7.Derriere, S. (1998) D.E.N.I.S strip 3792: {http://cdsweb.ustrasbg.fr/DENIS/qual_gif/cpl3792.dat}]]Google Scholar
- 8.Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994). Fast subsequence matching in time-series databases. In Proc. ACM SIGMOD Conf., Minneapolis, May.]] Google ScholarDigital Library
- 9.Gavrila, D. M. & Davis,L. S.(1995). Towards 3-d modelbased tracking and recognition of human movement: a multiview approach. In International Workshop on Automatic Face- and Gesture-Recognition. IEEE Computer Society.]]Google Scholar
- 10.Gollmer, K., & Posten, C. (1995) Detection of distorted pattern using dynamic time warping algorithm and application for supervision of bioprocesses. On-Line Fault Detection and Supervision in Chemical Process Industries.]]Google Scholar
- 11.Kadous, M. W. (1999) Learning comprehensible descriptions of multivariate time series. In Proc. of the 16 th International Machine Learning Conference. Morgan Kaufmann.]] Google ScholarDigital Library
- 12.Keogh, E., & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 239-241, AAAI Press.]]Google Scholar
- 13.Keogh, E., & Pazzani, M. (1999). An indexing scheme for fast similarity search in large time series databases. In Proc. of the 11 th International Conference on Scientific and Statistical Database Management.]] Google ScholarDigital Library
- 14.Keogh, E., & Pazzani, M. (2000). A simple dimensionality reduction technique for fast similarity search in large time series databases. In 4 th Pacific- Asia Conference on Knowledge Discovery and Data Mining. Kyoto, Japan]] Google ScholarDigital Library
- 15.Keogh, E., Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. Proc. of the 3 rd International Conference of Knowledge Discovery and Data Mining. pp 24-20, AAAI Press.]]Google Scholar
- 16.Kruskall, J. B. & Liberman, M. (1983). The symmetric time warping algorithm: From continuous to discrete. In Time Warps, String Edits and Macromolecules. Addison-Wesley.]]Google Scholar
- 17.Manganaris, S. (1997). Supervised classification with temporal data. PhD thesis, Computer Science Department, School of Engineering, Vanderbilt University]] Google ScholarDigital Library
- 18.Rabiner, L. & Juang, B. (1993). Fundamentals of speech recognition. Englewood Cliffs, N.J, Prentice Hall.]] Google ScholarDigital Library
- 19.Saito, N. (1994). Local feature extraction and its application using a library of bases. PhD thesis, Yale University.]] Google ScholarDigital Library
- 20.Sakoe, H. & Chiba, S. (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., Vol. ASSP-26.]]Google Scholar
- 21.Schmill, M., Oates, T. & Cohen, P. (1999). Learned models for continuous planning. In Seventh International Workshop on Artificial Intelligence and Statistics.]]Google Scholar
- 22.Shatkay, H., & Zdonik, S. (1996). Approximate queries and representations for large data sequences. Proc. 12 th IEEE International Conference on Data Engineering. pp 546-553.]] Google ScholarDigital Library
- 23.Yi, B. K., Jagadish, H. V., Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. In Proc. of the 14 th International Conference on Data Engineering. pp 201-208.]] Google ScholarDigital Library
Index Terms
- Scaling up dynamic time warping for datamining applications
Recommendations
Extracting Optimal Performance from Dynamic Time Warping
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningDynamic Time Warping (DTW) is a distance measure that compares two time series after optimally aligning them. DTW is being used for decades in thousands of academic and industrial projects despite the very expensive computational complexity, O(n2). ...
A method for measuring similarity of time series based on series decomposition and dynamic time warping
AbstractDynamic time warping (DTW) is one of the most important similarity measurement methods for time series analysis. In view of the high complexity and pathological alignment of DTW, a lot of variants of DTW have been proposed. However, the existing ...
Flexible Dynamic Time Warping for Time Series Classification
Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y , the dynamic time warping (DTW) algorithm can calculate the distance ...
Comments