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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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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