Information Technology Reference
In-Depth Information
17. J. Lines, A. Bagnall, P. Caiger-Smith, S. Anderson, Classification of household devices by
electricity usage profiles, in IDEAL , pp. 403-412 (2011)
18. N. Marwan, Encounters with Neighbours: Current Developments of Concepts Based on Recur-
rence Plots and their Applications . Ph.D. thesis, University of Potsdam (2003)
19. N. Marwan, M. Romano, M. Thiel, Recurrence plots and cross recurrence plots. www.
recurrence-plot.tk
20. N. Marwan, S. Schinkel, J. Kurths, Recurrence plots 25 years later—gaining confidence in
dynamical transitions. Europhys. Lett. 101 (2), (2013)
21. N. Marwan, M. Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex
systems. Phys. Rep. 438 (5-6), 237-329 (2007)
22. N. Marwan, A historical review of recurrence plots. Eur. Phys. J. Spec. Top. 164 (1), 3-12
(2008)
23. N. Marwan, How to avoid potential pitfalls in recurrence plot based data analysis. Int. J. Bifurc.
Chaos 21 (4), 1003-1017 (2011)
24. U. Maulik, S. Bandyopadhyay, Performance evaluation of some clustering algorithms and
validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24 (12), 1650-1654 (2002)
25. W. Meesrikamolkul, V. Niennattrakul, C.A. Ratanamahatana, Shape-based clustering for time
series data, in PA K D D , pp. 530-541 (2012)
26. C.S. Moeller-Levet, F. Klawonn, K.-H. Cho, O. Wolkenhauer, Fuzzy clustering of short time-
series and unevenly distributed sampling points, in LNCS, Proceedings of the IDA2003 ,
pp. 28-30 (2003)
27. T. Rakthanmanon, B.J.L. Campana, A. Mueen, G. Batista, M.B. Westover, Q. Zhu, J. Zakaria,
E.J. Keogh, Searching and mining trillions of time series subsequences under dynamic time
warping, in KDD , pp. 262-270 (2012)
28. T. Rakthanmanon, E.J. Keogh, Fast-shapelets: a scalable algorithm for discovering time series
shapelets, in SDM (2013)
29. H. Sakoe, S. Chiba, Dynamic programming algorithm optimization for spoken word recogni-
tion. Trans. Acoust. Speech Signal Process. 26 (1) (1978)
30. S. Salvador, P. Chan, Toward accurate dynamic time warping in linear time and space. J. Intell.
Data Anal. 11 (5), 561-580 (2007)
31. A.P. Schultz, Y. Zou, N. Marwan, M.T. Turvey, Local minima-based recurrence plots for con-
tinuous dynamical systems. Int. J. Bifurc. Chaos 21 (4), 1065-1075 (2011)
32. S. Spiegel, S. Albayrak, An order-invariant time series distancemeasure—position on recent
developments in time series analysis, in Proceedings of 4th International Conference on Knowl-
edge Discovery and Information Retrieval (KDIR) (SciTePress, 2012), pp. 264-268
33. S. Spiegel, J. Gaebler, A. Lommatzsch, E. De Luca, S. Albayrak, Pattern recognition and
classification for multivariate time series, in Proceedings of the 5th International Workshop on
Knowledge Discovery from Sensor Data, SensorKDD'11 (ACM, New York, 2011), pp. 34-42
34. S. Spiegel, B.-J. Jain, S. Albayrak, A recurrence plot-based distance measure, in Springer
Proceedings in Mathematics—Translational Recurrences: From Mathematical Theory to Real-
World Applications (2014). To appear
35. S. Spiegel, B.-J. Jain, E. De Luca, S. Albayrak, Pattern recognition in multivariate time series—
dissertation proposal, in Proceedings of 4th Workshop for Ph.D. Students in Information and
Knowledge Management (PIKM) , CIKM'11 (ACM, 2011)
36. S. Spiegel, D. Schultz, M. Schacht, S. Albayrak, Supplementary onlinematerial—besttime
App, test data, video demonstration. Technical report: www.dai-lab.de/spiegel/besttime.html
(2013)
37. E.I. Vlahogianni, M.G. Karlaftis, Comparing traffic flow time-series under fine and adverse
weather conditions using recurrence-based complexity measures. J. Nonlinear Dyn. 69 (4),
1949-1963 (2012)
38. C.L. Webber, N. Marwan, A. Facchini, A. Giuliani, Simpler methods do it better: success of
recurrence quantification analysis as a general purpose data analysis tool. Phys. Lett. A 373 (41),
3753-3756 (2009)
Search WWH ::




Custom Search