Abstract
This paper begins with a new algorithm for computing time sequence data expansion distance on the time domain that, with a time complexity of O(n×m), solves the problem of retained similarity after the shifting and scaling of time sequence on the Y axis. After this, another algorithm is proposed for computing time sequence data expansion distance on frequency domain and searching similar subsequence in long time sequence, with a time complexity of merely O(n×fc), suitable for online implementation for its high efficiency, and adaptable to the extended definition of time sequence data expansion distance. An incremental DFT algorithm is also provided for time sequence data and linear weighted time sequence data, which allows dimension reduction on each window of a long sequence, simplifying the traditional O(n×m×fc) to O(n×fc).
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Zheng, Q., Feng, Z., Zhu, M. (2005). Incremental DFT Based Search Algorithm for Similar Sequence. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_148
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DOI: https://doi.org/10.1007/11539506_148
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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