Using derivatives in time series classification

Data Mining and Knowledge Discovery

Volumn 26, Issue 2, 2013, Pages 310-331

  Górecki, Tomasz ^a   Łuczak, MacIej ^b  

^a ADAM MICKIEWICZ UNIVERSITY   (Poland)

^b KOSZALIN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Data mining; Derivative Dynamic Time Warping; Dynamic Time Warping; Time series

Indexed keywords

BIOLOGICAL OPERATIONS; DATA SETS; DISTANCE FUNCTIONS; DISTANCE MEASURE; DYNAMIC TIME WARPING; MODERN INFORMATION TECHNOLOGIES; NEAREST NEIGHBOR RULE; TIME SERIES CLASSIFICATIONS;

CLASSIFICATION (OF INFORMATION); DATA MINING; EXPERIMENTS; INFORMATION TECHNOLOGY;

TIME SERIES;

EID: 84872404670     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-012-0251-4     Document Type: Article

References (26)

1. Batista G, Wang X, Keogh E (2011) A complexity-invariant distance measure for time series. In: Eleventh SIAM international conference on data mining (SDM'2011), Mesa, USA
2. Benedikt L, Cosker D, Rosin PL, Marshal D (2008) Facial dynamics in biometric identification. In: BMVC, vol 2, pp 235- 241
3. Benedikt L, Cosker D, Rosin PL, Marshal D (2010) Assessing the uniqueness and permanence of facial actions for use in biometric applications. IEEE Trans Syst Man Cybernet A Syst Humans 40(3): 449-460
4. Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: AAAI workshop on knowledge discovery in databases, pp 229-248
5. Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: forecasting and control. Wiley, New York
6. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7: 1-30
7. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proceedings of 34th international conference on very large data bases, pp 1542-1552
8. Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56: 52-64
9. Eads D, Hill D, Davis S, Perkins S, Ma J, Porter R, Theiler J (2002) Genetic algorithms and support vector machines for time series classification. In: Proc Int Soc Optic Eng 4787: 74-85
10. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32: 675-701
11. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11: 86-92
12. Gullo F, Ponti F, Tagarelli A, Greco S (2009) A time series representation model for accurate and fast similarity detection. Pattern Recogn 42(11): 2998-3014
13. Hollander M, Wolfe DA (1973) Nonparametric statistical methods. Wiley, New York
14. Iman RL, Davenport JM (1980) Approximations of the critical region of the Freidman statistic. Commun Stat Theory Methods 9: 571-595
15. Keogh E (2002) Exact indexing of dynamic time warping. In 28th International Conference on Very Large Data Bases 406-417
16. Keogh E, Kasetty E (2003) On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min Knowl Discov 7(4): 349-371
17. Keogh E, Pazzani M (2001) Dynamic time warping with higher order features. In: Proceedings of SIAM international conference on data mining (SDM'2001), Chicago, USA
18. Keogh E, Xi X, Wei L, Ratanamahatana CA (2006) The UCR Time Series Classification/Clustering Homepage: http://www.cs.ucr.edu/~eamonn/time-series- data/
19. Kulbacki M, Segen J, Bak A (2002) Unsupervised learning motion models using dynamic time warping. In: Proceedings of the intelligent information systems 2002 symposium, Sopot, 2002
20. Looney SW (1998) A statistical technique for comparing the accuracies of several classifiers. Pattern Recogn Lett 8: 5-9
21. Luan F, Li K, Ma S (2010) The algorithm of online handwritten signature verification based on improved DTW. Int J Model Identif Control 10(1-2): 81-86
22. Mokhtar N, Arof H, Iwahashi M (2010) One dimensional image processing for eye tracking using derivative dynamic time warping. Sci Res Essays 5(19): 2947-2952
23. Nemenyi PB (1963) Distribution-free multiple comparisons. PhD thesis, Princeton University
24. Pavlovic V, Frey BJ, Huang TS (1999) Time-series classification using mixed-state dynamic Bayesian networks. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol 2, pp 2609-2615
25. Penny W, Roberts S (1999) Dynamic models for nonstationary signal segmentation. Comput Biomed Res 32(6): 483-502
26. Petridis V, Kehagias A (1997) Predictive modular neural networks for time series classification. Neural Netw 10(1): 31-49