Abstract
The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.
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Project supported by the National High-Tech R&D Program (863) of China (Nos. 2012AA012600, 2011AA010702, 2012AA01A401, and 2012AA01A402), the National Natural Science Foundation of China (No. 60933005), and the National Science and Technology of China (No. 2012BAH38B04)
ORCID: Hong YIN, http://orcid.org/0000-0002-0682-6781
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Yin, H., Yang, Sq., Zhu, Xq. et al. Symbolic representation based on trend features for knowledge discovery in long time series. Frontiers Inf Technol Electronic Eng 16, 744–758 (2015). https://doi.org/10.1631/FITEE.1400376
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DOI: https://doi.org/10.1631/FITEE.1400376