Сравнительный анализ мер сходства, основанных на преобразовании скользящих аппроксимаций, в задачах классификации временных рядов
https://doi.org/10.15514/ISPRAS-2016-28(6)-15
Аннотация
Ключевые слова
Об авторах
И. С. АлимоваРоссия
В. Д. Соловьев
Россия
И. З. Батыршин
Россия
Список литературы
1. Weiss S. M. A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets. Proceedings of the 2012 SIAM International Conference on Data Mining, 2012, pp. 999-1010. DOI: 10.1137/1.9781611972825.86.
2. Giusti R., Batista G. E. An empirical comparison of dissimilarity measures for time series classification. Intelligent Systems (BRACIS), 2013 Brazilian Conference on. - IEEE, 2013, pp. 82-88.
3. Ding H. et al. Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment, 2015, vol. 1, issue 2, pp. 1542-1552.
4. Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., & Suarez, R. Moving approximations in time series data mining. Proc. Int. Conf. Fuzzy Sets and Soft Computing in Economics and Finance FSSCEF, 2004, pp. 62-72.
5. Almanza V., Batyrshin I. On trend association analysis of time series of atmospheric pollutants and meteorological variables in Mexico City Metropolitan Area. Mexican Conference on Pattern Recognition. Springer Berlin Heidelberg, 2011, pp. 95-102.
6. Батыршин И.З., Кошульски А., Шереметов Л.Б., Климова А.С., Панов А.М. Анализ взаимодействия нефтяных скважин на основе гибридной кластеризации временных рядов продуктивности скважин. Нечеткие системы и мягкие вычисления. Тверской государственный университет, том 2, вып. 4, 2007 г., стр. 63-73.
7. E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana. (2006) The UCR time series classification/clustering homepage. Доступно по ссылке: http://www. cs. ucr. edu/˜ eamonn/time series data.
8. M. Muller. Dynamic time warping. Inf. Retr. Music Motion. Information retrieval for music and motion. Springer, Berlin, 2007, pp. 69-84.
9. Lu G. et al. A novel framework of change-point detection for machine monitoring. Mechanical Systems and Signal Processing, 2017, vol. 83, pp. 533-548.
10. Rath T. M., Manmatha R. Word image matching using dynamic time warping. Computer Vision and Pattern Recognition. Proceedings IEEE Computer Society Conference on, 2003, vol. 2, pp. 521-527.
11. Muda L., Begam M., Elamvazuthi I. Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. Journal of Computing, 2010, vol. 2, issue 3, pp. 138-143.
12. Vakanski A. et al. Trajectory learning for robot programming by demonstration using hidden Markov model and dynamic time warping. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, vol. 42, issue 4, pp. 1039-1052.
13. Keogh E. J., Pazzani M. J. Derivative Dynamic Time Warping.Sdm, 2001, vol. 1, pp. 5-7.
14. Faloutsos C., Ranganathan M., Manolopoulos Y. Fast subsequence matching in time-series databases. Proceedings of the 1994 ACM SIGMOD international Conference on Management of Data, 1994, vol. 23, issue 2, pp. 419-429.
Рецензия
Для цитирования:
Алимова И.С., Соловьев В.Д., Батыршин И.З. Сравнительный анализ мер сходства, основанных на преобразовании скользящих аппроксимаций, в задачах классификации временных рядов. Труды Института системного программирования РАН. 2016;28(6):207-222. https://doi.org/10.15514/ISPRAS-2016-28(6)-15
For citation:
Alimova I.S., Solovyev V.D., Batyrshin I.Z. Comparative analysis of the similarity measures based on the moving approximation transformation in problems of time series classification. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2016;28(6):207-222. (In Russ.) https://doi.org/10.15514/ISPRAS-2016-28(6)-15