Fast fuzzy subsequence matching algorithms on time-series

•Several techniques are used to accelerate computational time.•R*-tree is adopted to further prune unnecessary computations.•Three fuzzy subsequence matching algorithms are compared.•Experiments are conducted on synthetic and real data set. Subsequence matching algorithms have many applications on t...

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Veröffentlicht in:Expert systems with applications Jg. 116; S. 275 - 284
Hauptverfasser: Gong, Xueyuan, Fong, Simon, Si, Yain-Whar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.02.2019
Elsevier BV
Schlagworte:
Algorithms
Euclidean geometry
Fuzzy logic
Fuzzy subsequence matching
IFSM-kd
IFSM-R
Matching
Neural networks
Subsequence matching
Time series
IFSM-kd
Fuzzy subsequence matching
Time-series
Subsequence matching
IFSM-R
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•Several techniques are used to accelerate computational time.•R*-tree is adopted to further prune unnecessary computations.•Three fuzzy subsequence matching algorithms are compared.•Experiments are conducted on synthetic and real data set. Subsequence matching algorithms have many applications on time-series, such as detecting specific patterns on Electrocardiogram (ECG) and temperature data. To the best of author’s knowledge, there are relatively few research studies on time-series fuzzy subsequence matching yet, which better expresses the logic in real life compared to exact subsequence matching. In this paper, we firstly propose Naive Fuzzy Subsequence Matching based on Euclidean Distance (NFSM-ED) and Dynamic Time Warping (NFSM-DTW) for solving fuzzy subsequence matching problem on time-series, which can be treated as a basic benchmark of efficiency and accuracy. Then we extend it to a novel approach called UCR Fuzzy Subsequence Matching (UFSM) algorithm, which is inspired by UCRSuite. Finally, we develop it to Improved Fuzzy Subsequence Matching by kd-tree (IFSM-kd) and R*-tree (IFSM-R*), which can efficiently and effectively perform fuzzy subsequence matching on time-series. Additionally, the experiment results show that IFSM-R* and IFSM-kd are much faster than NFSM-ED, NFSM-DTW and UFSM with nearly no extra memory space required. Furthermore, IFSM-R* supports inserting and deleting indexes compared to IFSM-kd.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.09.011