Abstract
Time series analysis has become an important field of data mining in the last decade. Dynamics of real-world processes are important in domains like seismology, medicine, astrophysics, meteorology, economics and industry. In this article we develop a methodology for analysing a periodical and multi-dimensional time series so as to extract new features that improve the performance of time series classification. Our aim is to have a methodology which is independent on the measurement errors and on the level of noise. For this we analyse three methods for extracting a period, both from the perspective of methodology and performance. We experimentally compare these strategies in order to identify the minimal quantity of labelled time series required for training so as to obtain a good classification accuracy.
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References
Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE. Trans. Comput. 100(1), 90–93 (1974)
Bagnall, A., Bostrom, A., Large, J., Lines, J.: The great time series classification bake off: An experimental evaluation of recently proposed algorithms. extended version (2016)
Cardoso, A.M., Cruz, S., Carvalho, J., Saraiva, E.: Rotor cage fault diagnosis in three-phase induction motors, by park’s vector approach. In: IEEE Industry Applications Conference, vol. 1, pp. 642–646. IEEE (1995)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, vol. 23. ACM (1994)
Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)
Fulcher, B.D.: Feature-based time-series analysis (2017)
Haar, A.: Zur theorie der orthogonalen funktionensysteme 69(3), 331–371 (1910)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
Hasna, O.L., Potolea, R.: Time series–a taxonomy based survey. In: Intelligent Computer Communication and Processing (ICCP), pp. 231–238. IEEE (2017)
Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., Osornio-Rios, R.A., Romero-Troncoso, R.J.: Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors. ISA Trans. 80, 427–438 (2018)
Mitsa, T.: Temporal Data Mining. Chapman and Hall/CRC, Boca Raton (2010)
Nandi, S., Toliyat, H., Li, X.: Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)
Rajakarunakaran, S., Venkumar, P., Devaraj, D., Rao, K.S.P.: Artificial neural network approach for fault detection in rotary system. Appl. Soft Comput. 8(1), 740–748 (2008)
Timmer, J., Gantert, C., Deuschl, G., Honerkamp, J.: Characteristics of hand tremor time series. Biol. Cybern. 70(1), 75–80 (1993)
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Disc. 26(2), 275–309 (2013)
Xu, B., Sun, L., Xu, L., Xu, G.: Improvement of the hilbert method via esprit for detecting rotor fault in induction motors at low slip. IEEE Trans. Energy Convers. 28(1), 225–233 (2013)
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Hasna, O.L., Potolea, R. (2018). Analysing a Periodical and Multi-dimensional Time Series. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_24
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DOI: https://doi.org/10.1007/978-3-030-05918-7_24
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