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Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data

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Modern Approaches for Intelligent Information and Database Systems


Classification is one of the most prevalent tasks in time series mining. Dynamic Time Warping and Longest Common Subsequence are well-known and widely used algorithms to measure similarity between two time series sequences using non-linear alignment. However, these algorithms work at its best when the time series pair has similar amplitude scaling, as a little adjustment of scale can actually double the error rates. Unfortunately, sensor data and most real-world time series data usually contain noise, missing values, outlier, and variability or scaling in both axes, which is not suitable for the widely used Z-normalization. We introduce the Local Feature Normalization (LFN) and its Local Scaling Feature (LSF), which can be used to robustly normalize noisy/warped/missing-valued time series. In addition, we utilize LSF to match time series containing multiple subsequences with a variety of scales; this algorithm is called Longest Common Local Scaling Feature (LCSF). Comparing to the usage of Z-normalized data, our classification results show that our proposed LFN is impressively robust, especially on high-error and noisy datasets. On both synthetic and real application data for wrist strengthening rehabilitation exercise using a mobile phone sensor, our LCSF similarity measure also significantly outperforms other existing methods by a large margin.

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Correspondence to Chotirat Ann Ratanamahatana .

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Chonbodeechalermroong, A., Ratanamahatana, C.A. (2018). Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham.

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  • Print ISBN: 978-3-319-76080-3

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