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...
Uloženo v:
| Vydáno v: | Expert systems with applications Ročník 116; s. 275 - 284 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Elsevier Ltd
01.02.2019
Elsevier BV |
| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| 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. |
|---|---|
| AbstractList | 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. •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. |
| 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 |
| BookMark | eNp9kD1PwzAQhi1UJNrCH2CKxJzgi5PYllhQxZdUiQVmy3Hs1lETF9sBtb-eRGVi6HTL-7x39yzQrHe9RugWcAYYqvs20-FHZjkGlmGeYYALNAdGSVpRTmZojnlJ0wJocYUWIbQYA8WYzhF_liEmZjgeD0kY6qC_Bt0rnXQyqq3tN4ncbZy3cduFxPVJtJ1Og_ZWh2t0aeQu6Ju_uUSfz08fq9d0_f7ytnpcp4rkLKbASMEUMXVNicQ453XDjCk5JUBMwRpFm0aTkilQNS0b4JXkhtWqLpVmVaHIEt2devfejceFKFo3-H5cKXIgwHLOGB9T7JRS3oXgtRHKRhmt66OXdicAi0mUaMUkSkyiBOZiFDWi-T90720n_eE89HCC9Pj6t9VeBGUnc431WkXROHsO_wX9o4SQ |
| CitedBy_id | crossref_primary_10_1111_2041_210X_13717 crossref_primary_10_1016_j_cie_2022_108039 crossref_primary_10_1007_s00521_023_09291_5 crossref_primary_10_1109_JPHOTOV_2020_2981833 crossref_primary_10_14778_3407790_3407827 |
| Cites_doi | 10.1007/s10115-004-0154-9 10.1016/j.ins.2010.11.031 10.1016/j.asoc.2015.10.012 10.1016/j.fss.2009.04.013 10.3390/rs10040654 10.1145/361002.361007 10.1007/s10618-012-0250-5 10.1109/TKDE.2013.8 10.1016/j.engappai.2014.12.015 10.1023/A:1024988512476 10.1016/j.eswa.2006.12.013 10.1093/bioinformatics/bty246 10.1007/s10115-004-0172-7 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Ltd Copyright Elsevier BV Feb 2019 |
| Copyright_xml | – notice: 2018 Elsevier Ltd – notice: Copyright Elsevier BV Feb 2019 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2018.09.011 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| EndPage | 284 |
| ExternalDocumentID | 10_1016_j_eswa_2018_09_011 S0957417418305827 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABBOA ABFNM ABKBG ABMAC ABMVD ABUCO ABXDB ABYKQ ACDAQ ACGFS ACHRH ACNNM ACNTT ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SET SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 8FD AFXIZ AGCQF AGRNS JQ2 L7M L~C L~D SSH |
| ID | FETCH-LOGICAL-c328t-18348c3fbb73a0029bd8ff597313f48dc7dde358c1cb75d196a9f8bcb5ce864c3 |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000449240500021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Mon Jul 14 09:34:18 EDT 2025 Tue Nov 18 22:35:04 EST 2025 Sat Nov 29 06:14:29 EST 2025 Fri Feb 23 02:24:25 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | IFSM-kd Fuzzy subsequence matching Time-series Subsequence matching IFSM-R |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-18348c3fbb73a0029bd8ff597313f48dc7dde358c1cb75d196a9f8bcb5ce864c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-1848-7246 0000-0001-8468-6182 0000-0002-5025-8216 |
| PQID | 2131829889 |
| PQPubID | 2045477 |
| PageCount | 10 |
| ParticipantIDs | proquest_journals_2131829889 crossref_citationtrail_10_1016_j_eswa_2018_09_011 crossref_primary_10_1016_j_eswa_2018_09_011 elsevier_sciencedirect_doi_10_1016_j_eswa_2018_09_011 |
| PublicationCentury | 2000 |
| PublicationDate | February 2019 2019-02-00 20190201 |
| PublicationDateYYYYMMDD | 2019-02-01 |
| PublicationDate_xml | – month: 02 year: 2019 text: February 2019 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Wang, Kam, Xiao, Bowen, Chaovalitwongse (bib0029) 2016 Shokoohi-Yekta, Chen, Campana, Hu, Zakaria, Keogh (bib0026) 2015 Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., & Batista, G. (2015). The ucr time series classification archive. URL Bock, Gumbsch, Moor, Rieck, Roqueiro, Borgwardt (bib0004) 2018; 34 Keogh, Kasetty (bib0014) 2003; 7 Bentley (bib0003) 1975; 18 Ravikumar, Devi (bib0023) 2013 Niennattrakul, Wanichsan, Ratanamahatana (bib0020) 2010 Ulanova, Begum, Keogh (bib0027) 2015 Petitjean, Forestier, Webb, Nicholson, Chen, Keogh (bib0021) 2014 Anand, Pal, Suganthan (bib0001) 2010 Beckmann, Kriegel, Schneider, Seeger (bib0002) 1990 Wang, Mueen, Ding, Trajcevski, Scheuermann, Keogh (bib0030) 2013; 26 Yang, He, Xu, Ni, Jones, Samatova (bib0031) 2018 Gong, Fong, Chan, Mohammed (bib0010) 2015 Cheng, Cheng, Wang (bib0007) 2008; 34 Liu, Zhan, Yang, Wang (bib0017) 2018; 10 Faloutsos, Ranganathan, Manolopoulos (bib0009) 1994 Yingchareonthawornchai, Sivaraks, Rakthanmanon, Ratanamahatana (bib0032) 2013 Keogh, Ratanamahatana (bib0013) 2005; 7 Kim (bib0015) 2014; 26 D’Urso, Maharaj (bib0008) 2009; 160 Mueen, Keogh, Zhu, Cash, Westover (bib0019) 2009; 9 Izakian, Pedrycz, Jamal (bib0011) 2015; 39 Rakthanmanon, Campana, Mueen, Batista, Westover, Zhu (bib0022) 2012 Rodpongpun, Niennattrakul, Ratanamahatana (bib0024) 2011; 5 Wan, Gong, Si (bib0028) 2016; 38 . Lin, Keogh, Lonardi, Chiu (bib0016) 2003 Sakurai, Faloutsos, Yamamuro (bib0025) 2007 Keogh, Lin (bib0012) 2005; 8 Maharaj, D’Urso (bib0018) 2011; 181 Zakaria, Mueen, Keogh (bib0033) 2012 Calin (bib0005) 2016 Lin (10.1016/j.eswa.2018.09.011_bib0016) 2003 Wang (10.1016/j.eswa.2018.09.011_bib0030) 2013; 26 Ulanova (10.1016/j.eswa.2018.09.011_bib0027) 2015 Niennattrakul (10.1016/j.eswa.2018.09.011_bib0020) 2010 Kim (10.1016/j.eswa.2018.09.011_bib0015) 2014; 26 Bentley (10.1016/j.eswa.2018.09.011_bib0003) 1975; 18 Keogh (10.1016/j.eswa.2018.09.011_bib0014) 2003; 7 Wang (10.1016/j.eswa.2018.09.011_bib0029) 2016 Sakurai (10.1016/j.eswa.2018.09.011_bib0025) 2007 Cheng (10.1016/j.eswa.2018.09.011_bib0007) 2008; 34 Petitjean (10.1016/j.eswa.2018.09.011_bib0021) 2014 Yingchareonthawornchai (10.1016/j.eswa.2018.09.011_bib0032) 2013 Calin (10.1016/j.eswa.2018.09.011_bib0005) 2016 Rodpongpun (10.1016/j.eswa.2018.09.011_bib0024) 2011; 5 Anand (10.1016/j.eswa.2018.09.011_bib0001) 2010 Faloutsos (10.1016/j.eswa.2018.09.011_bib0009) 1994 Wan (10.1016/j.eswa.2018.09.011_bib0028) 2016; 38 Gong (10.1016/j.eswa.2018.09.011_bib0010) 2015 Mueen (10.1016/j.eswa.2018.09.011_bib0019) 2009; 9 Shokoohi-Yekta (10.1016/j.eswa.2018.09.011_bib0026) 2015 Keogh (10.1016/j.eswa.2018.09.011_bib0012) 2005; 8 Rakthanmanon (10.1016/j.eswa.2018.09.011_bib0022) 2012 Ravikumar (10.1016/j.eswa.2018.09.011_bib0023) 2013 Beckmann (10.1016/j.eswa.2018.09.011_bib0002) 1990 Izakian (10.1016/j.eswa.2018.09.011_bib0011) 2015; 39 Zakaria (10.1016/j.eswa.2018.09.011_bib0033) 2012 Bock (10.1016/j.eswa.2018.09.011_bib0004) 2018; 34 Liu (10.1016/j.eswa.2018.09.011_bib0017) 2018; 10 Maharaj (10.1016/j.eswa.2018.09.011_bib0018) 2011; 181 10.1016/j.eswa.2018.09.011_bib0006 Keogh (10.1016/j.eswa.2018.09.011_bib0013) 2005; 7 Yang (10.1016/j.eswa.2018.09.011_bib0031) 2018 D’Urso (10.1016/j.eswa.2018.09.011_bib0008) 2009; 160 |
| References_xml | – start-page: 322 year: 1990 end-page: 331 ident: bib0002 article-title: The R*-tree: an efficient and robust access method for points and rectangles publication-title: Proceedings of the 1990 ACM SIGMOD international conference on management of data SIGMOD ’90 – volume: 5 start-page: 2 year: 2011 ident: bib0024 article-title: Efficient subsequence search on streaming data based on time warping distance publication-title: Computer and Information Technology – start-page: 1265 year: 2013 end-page: 1270 ident: bib0032 article-title: Efficient proper length time series motif discovery publication-title: IEEE 13th international conference on data mining – volume: 34 start-page: 1235 year: 2008 end-page: 1242 ident: bib0007 article-title: Multi-attribute fuzzy time series method based on fuzzy clustering publication-title: Expert Systems with Applications – volume: 181 start-page: 1187 year: 2011 end-page: 1211 ident: bib0018 article-title: Fuzzy clustering of time series in the frequency domain publication-title: Information Sciences – start-page: 262 year: 2012 end-page: 270 ident: bib0022 article-title: Searching and mining trillions of time series subsequences under dynamic time warping publication-title: The 18th international conference on knowledge discovery and data mining – start-page: 785 year: 2012 end-page: 794 ident: bib0033 article-title: Clustering time series using unsupervised-shapelets publication-title: 12th IEEE international conference on data mining – start-page: 2 year: 2003 end-page: 11 ident: bib0016 article-title: A symbolic representation of time series, with implications for streaming algorithms publication-title: Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery – start-page: 900 year: 2015 end-page: 908 ident: bib0027 article-title: Scalable clustering of time series with u-shapelets publication-title: Proceedings of the 2015 SIAM international conference on data mining – volume: 26 start-page: 426 year: 2014 end-page: 437 ident: bib0015 article-title: Probabilistic sequence translation-alignment model for time-series classification publication-title: IEEE Transactions on Knowledge and Data Engineering – start-page: 1 year: 2013 end-page: 6 ident: bib0023 article-title: Fuzzy classification of time series data publication-title: IEEE international conference on fuzzy systems – start-page: 1 year: 2010 end-page: 8 ident: bib0001 article-title: Integration of functional information of genes in fuzzy clustering of short time series gene expression data publication-title: Proceedings of the IEEE congress on evolutionary computation – volume: 7 start-page: 349 year: 2003 end-page: 371 ident: bib0014 article-title: On the need for time series data mining benchmarks: a survey and empirical demonstration publication-title: Data Mining and Knowledge Discovery – start-page: 104 year: 2018 end-page: 118 ident: bib0031 article-title: An intelligent and hybrid weighted fuzzy time series model based on empirical mode decomposition for financial markets forecasting publication-title: Advances in data mining. applications and theoretical aspects - 18th industrial conference, ICDM – volume: 39 start-page: 235 year: 2015 end-page: 244 ident: bib0011 article-title: Fuzzy clustering of time series data using dynamic time warping distance publication-title: Engineering Applications of Artificial Intelligence – volume: 18 start-page: 509 year: 1975 end-page: 517 ident: bib0003 article-title: Multidimensional binary search trees used for associative searching publication-title: Communications of the Acm – volume: 34 start-page: i438 year: 2018 end-page: i446 ident: bib0004 article-title: Association mapping in biomedical time series via statistically significant shapelet mining publication-title: Bioinformatics – volume: 10 start-page: 654 year: 2018 ident: bib0017 article-title: Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology publication-title: Remote Sensing – start-page: 156 year: 2010 end-page: 167 ident: bib0020 article-title: Accurate subsequence matching on data stream under time warping distance publication-title: New frontiers in applied data mining – volume: 9 start-page: 473 year: 2009 end-page: 484 ident: bib0019 article-title: Exact discovery of time series motifs publication-title: Proceedings of the 2015 SIAM international conference on data mining – volume: 38 start-page: 346 year: 2016 end-page: 359 ident: bib0028 article-title: Effect of segmentation on financial time series pattern matching publication-title: Applied Soft Computing – volume: 26 start-page: 275 year: 2013 end-page: 309 ident: bib0030 article-title: Experimental comparison of representation methods and distance measures for time series data publication-title: Data Mining and Knowledge Discovery – start-page: 419 year: 1994 end-page: 429 ident: bib0009 article-title: Fast subsequence matching in time-series databases publication-title: Proceedings of the 1994 ACM SIGMOD international conference on management of data SIGMOD ’94 – start-page: 470 year: 2014 end-page: 479 ident: bib0021 article-title: Dynamic time warping averaging of time series allows faster and more accurate classification publication-title: IEEE international conference on data mining – start-page: 2159 year: 2016 end-page: 2165 ident: bib0029 article-title: An efficient time series subsequence pattern mining and prediction framework with an application to respiratory motion prediction publication-title: Proceedings of the thirtieth conference on artificial intelligence (AAAI) – start-page: 1 year: 2015 end-page: 25 ident: bib0010 article-title: Nspring: the spring extension for subsequence matching of time series supporting normalization publication-title: The Journal of Supercomputing – start-page: 264 year: 2016 end-page: 271 ident: bib0005 article-title: Gesture recognition on kinect time series data using dynamic time warping and hidden markov models publication-title: 18th international symposium on symbolic and numeric algorithms for scientific computing, SYNASC – reference: . – volume: 160 start-page: 3565 year: 2009 end-page: 3589 ident: bib0008 article-title: Autocorrelation-based fuzzy clustering of time series publication-title: Fuzzy Sets and Systems – start-page: 1085 year: 2015 end-page: 1094 ident: bib0026 article-title: Discovery of meaningful rules in time series publication-title: Proceedings of the 21th international conference on knowledge discovery and data mining – reference: Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., & Batista, G. (2015). The ucr time series classification archive. URL: – volume: 8 start-page: 154 year: 2005 end-page: 177 ident: bib0012 article-title: Clustering of time-series subsequences is meaningless: Implications for previous and future research publication-title: Knowledge and Information Systems – volume: 7 start-page: 358 year: 2005 end-page: 386 ident: bib0013 article-title: Exact indexing of dynamic time warping publication-title: Knowledge & Information Systems – start-page: 1046 year: 2007 end-page: 1055 ident: bib0025 article-title: Stream monitoring under the time warping distance publication-title: Proceedings of the 23rd international conference on data engineering – start-page: 785 year: 2012 ident: 10.1016/j.eswa.2018.09.011_bib0033 article-title: Clustering time series using unsupervised-shapelets – start-page: 2 year: 2003 ident: 10.1016/j.eswa.2018.09.011_bib0016 article-title: A symbolic representation of time series, with implications for streaming algorithms – start-page: 1046 year: 2007 ident: 10.1016/j.eswa.2018.09.011_bib0025 article-title: Stream monitoring under the time warping distance – start-page: 322 year: 1990 ident: 10.1016/j.eswa.2018.09.011_bib0002 article-title: The R*-tree: an efficient and robust access method for points and rectangles – volume: 7 start-page: 358 issue: 3 year: 2005 ident: 10.1016/j.eswa.2018.09.011_bib0013 article-title: Exact indexing of dynamic time warping publication-title: Knowledge & Information Systems doi: 10.1007/s10115-004-0154-9 – volume: 181 start-page: 1187 issue: 7 year: 2011 ident: 10.1016/j.eswa.2018.09.011_bib0018 article-title: Fuzzy clustering of time series in the frequency domain publication-title: Information Sciences doi: 10.1016/j.ins.2010.11.031 – volume: 38 start-page: 346 year: 2016 ident: 10.1016/j.eswa.2018.09.011_bib0028 article-title: Effect of segmentation on financial time series pattern matching publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.10.012 – volume: 160 start-page: 3565 issue: 24 year: 2009 ident: 10.1016/j.eswa.2018.09.011_bib0008 article-title: Autocorrelation-based fuzzy clustering of time series publication-title: Fuzzy Sets and Systems doi: 10.1016/j.fss.2009.04.013 – start-page: 104 year: 2018 ident: 10.1016/j.eswa.2018.09.011_bib0031 article-title: An intelligent and hybrid weighted fuzzy time series model based on empirical mode decomposition for financial markets forecasting – volume: 10 start-page: 654 issue: 4 year: 2018 ident: 10.1016/j.eswa.2018.09.011_bib0017 article-title: Characterizing the spatio-temporal pattern of land surface temperature through time series clustering: Based on the latent pattern and morphology publication-title: Remote Sensing doi: 10.3390/rs10040654 – volume: 9 start-page: 473 year: 2009 ident: 10.1016/j.eswa.2018.09.011_bib0019 article-title: Exact discovery of time series motifs – start-page: 262 year: 2012 ident: 10.1016/j.eswa.2018.09.011_bib0022 article-title: Searching and mining trillions of time series subsequences under dynamic time warping – start-page: 1265 year: 2013 ident: 10.1016/j.eswa.2018.09.011_bib0032 article-title: Efficient proper length time series motif discovery – volume: 18 start-page: 509 issue: 9 year: 1975 ident: 10.1016/j.eswa.2018.09.011_bib0003 article-title: Multidimensional binary search trees used for associative searching publication-title: Communications of the Acm doi: 10.1145/361002.361007 – start-page: 2159 year: 2016 ident: 10.1016/j.eswa.2018.09.011_bib0029 article-title: An efficient time series subsequence pattern mining and prediction framework with an application to respiratory motion prediction – start-page: 1 year: 2015 ident: 10.1016/j.eswa.2018.09.011_bib0010 article-title: Nspring: the spring extension for subsequence matching of time series supporting normalization publication-title: The Journal of Supercomputing – volume: 26 start-page: 275 issue: 2 year: 2013 ident: 10.1016/j.eswa.2018.09.011_bib0030 article-title: Experimental comparison of representation methods and distance measures for time series data publication-title: Data Mining and Knowledge Discovery doi: 10.1007/s10618-012-0250-5 – start-page: 1 year: 2013 ident: 10.1016/j.eswa.2018.09.011_bib0023 article-title: Fuzzy classification of time series data – volume: 5 start-page: 2 issue: 1 year: 2011 ident: 10.1016/j.eswa.2018.09.011_bib0024 article-title: Efficient subsequence search on streaming data based on time warping distance publication-title: Computer and Information Technology – volume: 26 start-page: 426 issue: 2 year: 2014 ident: 10.1016/j.eswa.2018.09.011_bib0015 article-title: Probabilistic sequence translation-alignment model for time-series classification publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2013.8 – volume: 39 start-page: 235 year: 2015 ident: 10.1016/j.eswa.2018.09.011_bib0011 article-title: Fuzzy clustering of time series data using dynamic time warping distance publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2014.12.015 – ident: 10.1016/j.eswa.2018.09.011_bib0006 – volume: 7 start-page: 349 issue: 4 year: 2003 ident: 10.1016/j.eswa.2018.09.011_bib0014 article-title: On the need for time series data mining benchmarks: a survey and empirical demonstration publication-title: Data Mining and Knowledge Discovery doi: 10.1023/A:1024988512476 – start-page: 1085 year: 2015 ident: 10.1016/j.eswa.2018.09.011_bib0026 article-title: Discovery of meaningful rules in time series – volume: 34 start-page: 1235 issue: 2 year: 2008 ident: 10.1016/j.eswa.2018.09.011_bib0007 article-title: Multi-attribute fuzzy time series method based on fuzzy clustering publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2006.12.013 – start-page: 470 year: 2014 ident: 10.1016/j.eswa.2018.09.011_bib0021 article-title: Dynamic time warping averaging of time series allows faster and more accurate classification – start-page: 900 year: 2015 ident: 10.1016/j.eswa.2018.09.011_bib0027 article-title: Scalable clustering of time series with u-shapelets – volume: 34 start-page: i438 issue: 13 year: 2018 ident: 10.1016/j.eswa.2018.09.011_bib0004 article-title: Association mapping in biomedical time series via statistically significant shapelet mining publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty246 – start-page: 264 year: 2016 ident: 10.1016/j.eswa.2018.09.011_bib0005 article-title: Gesture recognition on kinect time series data using dynamic time warping and hidden markov models – volume: 8 start-page: 154 issue: 2 year: 2005 ident: 10.1016/j.eswa.2018.09.011_bib0012 article-title: Clustering of time-series subsequences is meaningless: Implications for previous and future research publication-title: Knowledge and Information Systems doi: 10.1007/s10115-004-0172-7 – start-page: 156 year: 2010 ident: 10.1016/j.eswa.2018.09.011_bib0020 article-title: Accurate subsequence matching on data stream under time warping distance – start-page: 1 year: 2010 ident: 10.1016/j.eswa.2018.09.011_bib0001 article-title: Integration of functional information of genes in fuzzy clustering of short time series gene expression data – start-page: 419 year: 1994 ident: 10.1016/j.eswa.2018.09.011_bib0009 article-title: Fast subsequence matching in time-series databases |
| SSID | ssj0017007 |
| Score | 2.2930322 |
| 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... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 275 |
| 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 |
| URI | https://dx.doi.org/10.1016/j.eswa.2018.09.011 https://www.proquest.com/docview/2131829889 |
| Volume | 116 |
| WOSCitedRecordID | wos000449240500021&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELZg44EXfiMGA-WBt8pTEiex8zihVYDQhNQB5cmyHZulWtMpSWHbX885dpJ2iAmQeIkqq3Ec3-Xz2ffdHUKvM0NVXBiCifW0JkpJLFSmsCziFKyFWJEuHcPnD_T4mM3n-UdPa266cgK0qtjFRX7-X0UNbSBsGzr7F-IeOoUG-A1ChyuIHa5_JPipaNqJWV9dXU4aQAVPlZ6AZepok-Ls26ou29OlcxSUS43tuDyXcDFQ83Td-jzPfQTchq97oO14Ru98rS_Xo6JNffOsXI5u_llHHPgqygp_ORX15nmDDXHa4m4MgTAj68idJlKcRK7gzoF2WMoowRl1BRAHsI224NJVTfErb-yKxf0C6u58YXGgmx82U1TEusy0HqO3k2XP7EDsOACpwpTF9DbahYfkANm7h--O5u8HDxMNXSh9P3AfUOW4f9ef9Duj5dry3dkkJw_QPb-ZCA6dEjxEt3T1CN3vC3UEHrcfo9zqRNDpRLChE0GvE8GoE8GqCjZ04gn6ND06efMW-5oZWJGYtRjeO2GKGCkpEdblKgtmTGoLlBGTsEJRWM9IylSkJE0LwF-RGyaVTJVmWaLIU7RTrSr9DAWxoUanImSSRImMpIStBHQSikwoChuJPRT1k8KVTyhv65qc8Z45uOB2IrmdSB7mHCZyD02Ge85dOpUb_532c829QegMPQ6qceN9-71guP8yGx5HsHzFOWP583_s9gW6O34Q-2inrdf6JbqjvrdlU7_yCvYTudyQIg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fast+fuzzy+subsequence+matching+algorithms+on+time-series&rft.jtitle=Expert+systems+with+applications&rft.au=Gong%2C+Xueyuan&rft.au=Fong%2C+Simon&rft.au=Si%2C+Yain-Whar&rft.date=2019-02-01&rft.pub=Elsevier+Ltd&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=116&rft.spage=275&rft.epage=284&rft_id=info:doi/10.1016%2Fj.eswa.2018.09.011&rft.externalDocID=S0957417418305827 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |