A distance based time series classification framework

Information Systems - Tập 51 - Trang 27-42 - 2015
Hüseyin Kaya1, Şule Gündüz Öğüdücü2
1Computational Science and Engineering, Istanbul Technical University, Turkey#TAB#
2Computer Engineering, Istanbul Technical University, Turkey

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Ramsay, 1997

Sanger, 1977, DNA sequencing with chain-terminating inhibitors, Proc. Natl. Acad. Sci. USA, 74, 5463, 10.1073/pnas.74.12.5463

Thalange, 1996, Model of normal prepubertal growth, Arch. Dis. Child., 75, 427, 10.1136/adc.75.5.427

C. Middour, T.C. Klaus, J. Jenkins, D. Pletcher, M. Cote, H. Chandrasekaran, B. Wohler, F. Girouard, J.P. Gunter, K. Uddin, C. Allen, J. Hall, K. Ibrahim, B. Clarke, J. Li, S. McCauliff, E. Quintana, J. Sommers, B. Stroozas, P. Tenenbaum, J. Twicken, H. Wu, D. Caldwell, S. Bryson, P. Bhavsar, M. Wu, B. Stamper, T. Trombly, C. Page, E. Santiago, Kepler science operations center architecture, Proc. SPIE, 7740 (2010) 77401A-77401A-12.

Bashir, 2008, Reduced dynamic time warping for handwriting recognition based on multidimensional time series of a novel pen device, Int. J. Intell. Syst. Technol.,WASET, 3, 194

Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018

Ettre, 1993, Nomenclature for chromatography, Pure Appl. Chem., 65, 819, 10.1351/pac199365040819

Smith, 2015, LC-MS alignment in theory and practice: a comprehensive algorithmic review, Brief. Bioinform., 16, 104, 10.1093/bib/bbt080

Coakley, 2001, Alignment of noisy signals, IEEE Trans. Instrum. Meas., 50, 141, 10.1109/19.903892

Ding, 2008, Querying and mining of time series data, Proc. VLDB Endow., 1, 1542, 10.14778/1454159.1454226

Sakoe, 1978, Dynamic-programming algorithm optimization for spoken word recognition, IEEE Trans. Acoust. Speech Signal Process., 26, 43, 10.1109/TASSP.1978.1163055

Prekopcsak, 2012, Time series classification by class-specific Mahalanobis distance measures, Adv. Data Anal. Classif., 6, 185, 10.1007/s11634-012-0110-6

Andrade, 2004, Robust normalization of DNA chromatograms by regression for improved base-calling, J. Frankl. Inst.—Eng. Appl. Math., 341, 3, 10.1016/j.jfranklin.2003.12.006

Johnson, 2003, High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis, J. Chromatogr. A, 996, 141, 10.1016/S0021-9673(03)00616-2

E. Keogh, Q. Zhu, B. Hu, Y. Hao, X. Xi, L. Wei, C.A. Ratanamahatana, The UCR Time Series Classification/Clustering Homepage, www.cs.ucr.edu/~eamonn/time_series_data, 2011 (retrieved July 21, 2014).

Xing, 2010, A brief survey on sequence classification, ACM SIGKDD Explor. Newslett., 12, 40, 10.1145/1882471.1882478

Faloutsos, 1994, Fast subsequence matching in time-series databases, SIGMOD Rec., 23, 419, 10.1145/191843.191925

I. Popivanov, R. Miller, Similarity search over time-series data using wavelets, in: Proceedings of the 18th International Conference on Data Engineering, San Jose, CA, Feb 26–Mar 01, 2002, pp. 212–221.

F. Korn, H.V. Jagadish, C. Faloutsos, Efficiently supporting ad hoc queries in large datasets of time sequences, in: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data – SIGMOD ׳97, vol. 26, ACM Press, New York, NY, USA, 1997, pp. 289–300. doi:10.1145/253260.253332.

Listgarten, 2007, Difference detection in LC-MS data for protein biomarker discovery, Bioinformatics, 23, E198, 10.1093/bioinformatics/btl326

Nanopoulos, 2001, Feature-based classification of time-series data, Int. J. Comput. Res., 10, 49

Pham, 1992, Control chart pattern recognition using neural networks, J. Syst. Eng., 2, 256

Pham, 1994, Control chart pattern-recognition using learning vector quantization networks, Int. J. Prod. Res., 32, 721, 10.1080/00207549408956963

Gauri, 2010, Control chart pattern recognition using feature-based learning vector quantization, Int. J. Adv. Manuf. Technol., 48, 1061, 10.1007/s00170-009-2354-7

Alpaydin, 2010

Ney, 1999, Dynamic programming search for continuous speech recognition, IEEE Signal Process. Mag., 16, 64, 10.1109/79.790984

Atal, 1971, Speech analysis and synthesis by linear prediction of the speech wave, J. Acoust. Soc. Am., 50, 637, 10.1121/1.1912679

Vintsyuk, 1968, Speech discrimination by dynamic programming, Cybernetics, 4, 52, 10.1007/BF01074755

Bellman, 2003

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, E. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping, in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, pp. 262–270.

E. Keogh, L. Wei, X. Xi, S. hee Lee, M. Vlachos, Lb-keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures, in: VLDB, 2006, pp. 882–893.

Salvadora, 2007, Toward accurate dynamic time warping in linear time and space, Intell. Data Anal., 11, 561, 10.3233/IDA-2007-11508

Nielsen, 1998, Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping, J. Chromatogr. A, 805, 17, 10.1016/S0021-9673(98)00021-1

Itakura, 1975, Minimum prediction residual principle applied to speech recognition, IEEE Trans. Acoust. Speech Signal Process., 23, 67, 10.1109/TASSP.1975.1162641

Ratanamahatana, 2005, Three myths about dynamic time warping data mining, 506

Kaya, 2013, SAGA, Inf. Sci., 228, 113, 10.1016/j.ins.2012.12.012

Eilers, 2004, Parametric time warping, Anal. Chem., 76, 404, 10.1021/ac034800e

van Nederkassel, 2006, A comparison of three algorithms for chromatograms alignment, J. Chromatogr. A, 1118, 199, 10.1016/j.chroma.2006.03.114

Ramsay, 1998, Curve registration, J. R. Stat. Soc. Ser. B—Stat. Methodol., 60, 351, 10.1111/1467-9868.00129

Ramsay, 1998, Estimating smooth monotone functions, J. R. Stat. Soc. Ser. B—Stat. Methodol., 60, 365, 10.1111/1467-9868.00130

F. Zhou, F.D. la Torre, Canonical time warping for alignment of human behavior, in: Advances in Neural Information Processing Systems, 2009.

Hardoon, 2004, Canonical correlation analysis, Neural Comput., 16, 2639, 10.1162/0899766042321814

D. Gong, G. Medioni, Dynamic manifold warping for view invariant action recognition, in: 2011 IEEE International Conference on Computer Vision (ICCV), IEEE, Barcelona, Spain, 2011, pp. 571–578.

H.T. Vu, C. Carey, S. Mahadevan, Manifold warping: manifold alignment over time., in: AAAI, 2012.

F. Zhou, F. De la Torre, Generalized time warping for multi-modal alignment of human motion, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Providence, Rhode Island, 2012, pp. 1282–1289.

Y. Zhao, R and Data Mining Examples and Case Studies, Academic Press, S.l, 2013.

Hall, 2009, The weka data mining software, SIGKDD Explor. Newslett., 11, 10, 10.1145/1656274.1656278

Chang, 2011, Libsvm: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 27, 10.1145/1961189.1961199

H. Lei, B. Sun, A study on the dynamic time warping in kernel machines, in: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007. SITIS׳07. IEEE, 2007, pp. 839–845.

S. Gudmundsson, T.P. Runarsson, S. Sigurdsson, Support vector machines and dynamic time warping for time series, in: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008 (IEEE World Congress on Computational Intelligence). IEEE, Hong Kong, 2008, pp. 2772–2776.

C. Ratanamahatana, E. Keogh, Making time-series classification more accurate using learned constraints, in: Proceedings of the Fourth SIAM International Conference on Data Mining, 2004, pp. 11–22.

Lee, 2004, Contour matching for a fish recognition and migration monitoring system, 37

O.J.O. Soderkvist, Computer Vision Classification of Leaves from Swedish Trees (Master׳s thesis), Linkoping University, 2001.

Pham, 1998, Control chart pattern recognition using a new type of self-organizing neural network, Proc. Inst. Mech. Eng. Part I—J. Syst. Control Eng., 212, 115, 10.1243/0959651981539343

D. Roverso, Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks, in: 3rd ANS International Topical Meeting on Nuclear Plant Instrumentation, Control and Human–Machine Interface, vol. 20, Citeseer, 2000.

D. Vail, M. Veloso, Learning from accelerometer data on a legged robot, in: Proceedings of the 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, 2004.

A. Mueen, E. Keogh, N. Young, Logical-shapelets: an expressive primitive for time series classification, in: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Diego, CA, 2011, pp. 1154–1162.

J.A. Brady, Considering complexity: Image Matching with Time Series (Ph.D. thesis), University of California Riverside, 2007.

Tapp, 2003, Ftir spectroscopy and multivariate analysis can distinguish the geographic origin of extra virgin olive oils, J. Agric. Food Chem., 51, 6110, 10.1021/jf030232s

J. Sun, S. Papadimitriou, C. Faloutsos, Online latent variable detection in sensor networks, in: ICDE 2005. Proceedings. 21st International Conference on Data Engineering, IEEE, Tokyo, Japan, 2005, pp. 1126–1127.

Eads, 2002, Genetic algorithms and support vector machines for time series classification, vol. 4787, 74

D. Eads, K. Glocer, S. Perkins, J. Theiler, Grammar-guided feature extraction for time series classification, in: Proceedings of the 9th Annual Conference on Neural Information Processing Systems (NIPS׳05), Citeseer, 2005.

J.J. Van Wijk, E.R. Van Selow, Cluster and calendar based visualization of time series data, in: Proceedings. 1999 IEEE Symposium on Information Visualization, 1999 (Info Vis׳ 99), IEEE, San Francisco, CA, 1999, pp. 4–9.

R.T. Olszewski, Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data, Technical Report, DTIC Document, 2001.

Adiac: Automatic Diatom Identification and Classification. 〈http://rbg-web2.rbge.org.uk/ADIAC/〉.

Briandet, 1996, Discrimination of arabica and robusta in instant coffee by fourier transform infrared spectroscopy and chemometrics, J. Agric. Food Chem., 44, 170, 10.1021/jf950305a

A. Bagnall, L.M. Davis, J. Hills, J. Lines, Transformation based ensembles for time series classification, in: SDM, vol. 12, SIAM, Anaheim, CA, 2012, pp. 307–318.

Saito, 1995, Local discriminant bases and their applications, J. Math. Imaging Vis., 5, 337, 10.1007/BF01250288

Al-Jowder, 2002, Detection of adulteration in cooked meat products by mid-infrared spectroscopy, J. Agric. Food Chem., 50, 1325, 10.1021/jf0108967

A. Gandhi, Content-Based Image Retrieval: Plant Species Identification (Master׳s thesis), Oregon State University, 2002.

P. Senin, S. Malinchik, Sax-vsm: Interpretable time series classification using sax and vector space model, in: 2013 IEEE 13th International Conference on Data Mining (ICDM), IEEE, Dallas, Texas, 2013, pp. 1175–1180.

Rath, 2007, Word spotting for historical documents, Int. J. Doc. Anal. Recognit., 9, 139, 10.1007/s10032-006-0027-8

Jalba, 2005, Automatic diatom identification using contour analysis by morphological curvature scale spaces, Mach. Vis. Appl., 16, 217, 10.1007/s00138-005-0175-8

J.C. Felipe, A.J. Traina, C. Traina Jr, A new similarity measure for histograms applied to content-based retrieval of medical images, in: Proceedings of the 2006 ACM symposium on Applied Computing, ACM, Dijon, France, 2006, pp. 258–259.

B. Malek, M. Orozco, A. El Saddik, Novel shoulder-surfing resistant haptic-based graphical password, in: Proceedings of EuroHaptics, vol. 6, 2006.

Mallat, 1999

M.K. Jeong, J.-C. Lu, X. Huo, B. Vidakovic, D. Chen, Wavelet-based data reduction techniques for process fault detection, Technometrics 48(1) (2006).

L. Li, Fast Algorithms for Mining Co-evolving Time Series, Technical Report, DTIC Document, 2011.

L. Wei, E. Keogh, Semi-supervised time series classification, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Philadelphia, USA, 2006, pp. 748–753.

P. Geurts, Contributions to Decision Tree Induction: Bias/variance Tradeoff and Time Series Classification, 2002.

Liu, 2009, uwave, Pervasive Mob. Comput., 5, 657, 10.1016/j.pmcj.2009.07.007