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 |
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| Sprache: | Englisch |
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01.02.2019
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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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| AbstractList | •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. 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. |
| Author | Si, Yain-Whar Fong, Simon Gong, Xueyuan |
| Author_xml | – sequence: 1 givenname: Xueyuan orcidid: 0000-0002-5025-8216 surname: Gong fullname: Gong, Xueyuan email: yb47453@umac.mo, amoonfana@qq.com – sequence: 2 givenname: Simon orcidid: 0000-0002-1848-7246 surname: Fong fullname: Fong, Simon email: ccfong@umac.mo – sequence: 3 givenname: Yain-Whar orcidid: 0000-0001-8468-6182 surname: Si fullname: Si, Yain-Whar email: fstasp@umac.mo |
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| Keywords | IFSM-kd Fuzzy subsequence matching Time-series Subsequence matching IFSM-R |
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| Snippet | •Several techniques are used to accelerate computational time.•R*-tree is adopted to further prune unnecessary computations.•Three fuzzy subsequence matching... Subsequence matching algorithms have many applications on time-series, such as detecting specific patterns on Electrocardiogram (ECG) and temperature data. To... |
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| SubjectTerms | Algorithms Euclidean geometry Fuzzy logic Fuzzy subsequence matching IFSM-kd IFSM-R Matching Neural networks Subsequence matching Time series |
| Title | Fast fuzzy subsequence matching algorithms on time-series |
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