Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases

We introduce a new model of similarity of time sequences that captures the intuitive notion that two sequences should be considered similar if they have enough non-overlapping time-ordered pairs of subsequences thar are similar. The model allows the amplitude of one of the two sequences to be scaled by any suitable amount and its offset adjusted appropriately. Two subsequences are considered similar if one can be enclosed within an envelope of a specified width drawn around the other. The model also allows non-matching gaps in the matching subsequences. The matching subsequences need not be aligned along the time axis. Given this model of similarity, we present fast search techniques for discovering all similar sequences in a set of sequences. These techniques can also be used to find all (sub)sequences similar to a given sequence. We applied this matching system to the U.S. mutual funds data and discovered interesting matches.

[1]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[2]  I. Anderson,et al.  Graphs and Networks , 1981, The Mathematical Gazette.

[3]  David Sankoff,et al.  Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison , 1983 .

[4]  Klaus H. Hinrichs,et al.  The Grid File: A Data Structure to Support Proximity Queries on Spatial Objects , 1983, International Workshop on Graph-Theoretic Concepts in Computer Science.

[5]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[6]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[7]  Christos Faloutsos,et al.  The A dynamic index for multidimensional ob-jects , 1987, Very Large Data Bases Conference.

[8]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[9]  Martin Vingron,et al.  A fast and sensitive multiple sequence alignment algorithm , 1989, Comput. Appl. Biosci..

[10]  W. Eric L. Grimson,et al.  On the sensitivity of geometric hashing , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[11]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[12]  Rakesh Mohan,et al.  Multidimensional indexing for recognizing visual shapes , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  John C. Curlander,et al.  ψ-s correlation and dynamic time warping: two methods for tracking ice floes in SAR images , 1991, IEEE Trans. Geosci. Remote. Sens..

[14]  Udi Manber,et al.  Fast text searching: allowing errors , 1992, CACM.

[15]  Mikhail A. Roytberg.  A search for common patterns in many sequences , 1992, Comput. Appl. Biosci..

[16]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[17]  Isidore Rigoutsos,et al.  FLASH: a fast look-up algorithm for string homology , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  C. Faloutsos.  Eecient Similarity Search in Sequence Databases , 1993 .

[19]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.

[20]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[21]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[22]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[23]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[24]  Kaizhong Zhang,et al.  Combinatorial pattern discovery for scientific data: some preliminary results , 1994, SIGMOD '94.