quant: a minimalist interval method for time series classification
We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an ‘off the shelf’ classifier. This distillatio...
Saved in:
| Published in | Data mining and knowledge discovery Vol. 38; no. 4; pp. 2377 - 2402 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.07.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-5810 1573-756X |
| DOI | 10.1007/s10618-024-01036-9 |
Cover
Loading…
| Abstract | We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an ‘off the shelf’ classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 min using a single CPU core. |
|---|---|
| AbstractList | We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an ‘off the shelf’ classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 min using a single CPU core. We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an ‘off the shelf’ classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 min using a single CPU core. |
| Author | Schmidt, Daniel F. Webb, Geoffrey I. Dempster, Angus |
| Author_xml | – sequence: 1 givenname: Angus surname: Dempster fullname: Dempster, Angus email: angus.dempster@monash.edu organization: Monash University – sequence: 2 givenname: Daniel F. surname: Schmidt fullname: Schmidt, Daniel F. organization: Monash University – sequence: 3 givenname: Geoffrey I. surname: Webb fullname: Webb, Geoffrey I. organization: Monash University |
| BookMark | eNp9kE9LxDAQxYOs4O7qF_AU8BydNGmaetPFf7DgRcFbSNNUs7TpbpIKfnvrVhA87Gnm8H7z5r0FmvneW4TOKVxSgOIqUhBUEsg4AQpMkPIIzWleMFLk4m027kxykksKJ2gR4wYA8ozBHN3uBu3TNda4c951unUxYeeTDZ-6xZ1NH32Nmz7g5DqLow3ORmxaHaNrnNHJ9f4UHTe6jfbsdy7R6_3dy-qRrJ8fnlY3a2KYYIlUggow0oqm1pmV3GqaVdrwTBYF0IIxXue8ZrnIbZMJWlacg6mpqGzNTMkbtkQX091t6HeDjUlt-iH40VIxkILykspiVGWTyoQ-xmAbtQ1jrvClKKifrtTUlRq7UvuuVDlC8h9kXNqHS0G79jDKJjSOPv7dhr-vDlDflMF_xg |
| CitedBy_id | crossref_primary_10_1016_j_chemolab_2024_105279 crossref_primary_10_1007_s10618_024_01022_1 |
| Cites_doi | 10.1007/s10994-006-6226-1 10.1007/s10994-021-06057-9 10.1007/s10618-022-00844-1 10.1007/3-540-44794-6_10 10.1007/978-3-030-67658-2_38 10.1016/j.ins.2013.02.030 10.1007/978-3-031-09037-0_53 10.1007/1-84628-102-4_18 10.1007/s10618-020-00701-z 10.1109/JAS.2019.1911747 10.1109/TPAMI.2013.72 10.1145/3182382 10.1007/3-540-45372-5_29 10.1007/s10618-020-00710-y 10.1007/s10618-019-00617-3 10.1007/s10618-019-00647-x 10.3233/IDA-2001-5305 10.1007/s10618-015-0425-y 10.1007/s10618-016-0483-9 10.1007/978-3-030-29859-3_33 10.1007/978-3-031-09282-4_13 10.1007/s10618-023-00939-3 10.1007/s10618-023-00978-w 10.1016/j.eswa.2022.118923 10.58895/ksp/1000138532-7 10.1007/s10618-024-01022-1 10.1109/BigData50022.2020.9378424 10.1109/ICDM51629.2021.00113 10.1007/s10994-023-06395-w 10.1145/3132847.3132980 10.1109/BigData55660.2022.10020496 10.1145/967900.968015 10.1109/ICDM50108.2020.00107 10.1145/3447548.3467231 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024 – notice: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.1007/s10618-024-01036-9 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Research Library ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Research Library Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ProQuest Central China ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ABI/INFORM Global (Corporate) CrossRef |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics Computer Science |
| EISSN | 1573-756X |
| EndPage | 2402 |
| ExternalDocumentID | 10_1007_s10618_024_01036_9 |
| GrantInformation_xml | – fundername: Australian Research Council grantid: DP210100072; DP210100072; DP210100072 funderid: http://dx.doi.org/10.13039/501100000923 – fundername: Monash University |
| GroupedDBID | -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 203 29F 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6NX 78A 7WY 8AO 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EDO EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X J-C J0Z J9A JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV LAK LLZTM M0C M0N M2O M4Y MA- N2Q NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM OVD P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOS R89 R9I RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S27 S3B SAP SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7S Z7W Z7X Z7Y Z7Z Z81 Z83 Z88 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT 7SC 7XB 8AL 8FD 8FK ABRTQ JQ2 L.- L7M L~C L~D MBDVC PKEHL PQEST PQGLB PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c363t-b6160c8e6fda2e84ea12bac42877017334d54d3565ef2619b440cd16bed3c94f3 |
| IEDL.DBID | BENPR |
| ISSN | 1384-5810 |
| IngestDate | Sat Aug 16 17:42:45 EDT 2025 Tue Jul 01 00:40:33 EDT 2025 Thu Apr 24 22:55:24 EDT 2025 Fri Feb 21 02:39:40 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Subseries Time series classification Quantile Fast Interval |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-b6160c8e6fda2e84ea12bac42877017334d54d3565ef2619b440cd16bed3c94f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://link.springer.com/10.1007/s10618-024-01036-9 |
| PQID | 3086149187 |
| PQPubID | 43030 |
| PageCount | 26 |
| ParticipantIDs | proquest_journals_3086149187 crossref_primary_10_1007_s10618_024_01036_9 crossref_citationtrail_10_1007_s10618_024_01036_9 springer_journals_10_1007_s10618_024_01036_9 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20240700 2024-07-00 20240701 |
| PublicationDateYYYYMMDD | 2024-07-01 |
| PublicationDate_xml | – month: 7 year: 2024 text: 20240700 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Data mining and knowledge discovery |
| PublicationTitleAbbrev | Data Min Knowl Disc |
| PublicationYear | 2024 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Dau, Bagnall, Kamgar (CR8) 2019; 6 CR19 CR18 Dempster, Petitjean, Webb (CR9) 2020; 34 CR17 CR39 Tan, Dempster, Bergmeir (CR41) 2022; 36 CR38 Rodríguez, Alonso, Bramer, Coenen, Allen (CR35) 2005 Guillaume, Vrain, Elloumi, El Yacoubi, Granger, Yuen (CR16) 2022 Ismail Fawaz, Lucas, Forestier (CR21) 2020; 34 CR34 Baydoğan, Runger, Tuv (CR4) 2013; 35 CR11 CR10 CR32 CR31 Lubba, Sethi, Knaute (CR25) 2019; 33 Middlehurst, Large, Cawley, Hutter, Kersting, Lijffijt (CR29) 2021 Middlehurst, Large, Flynn (CR30) 2021; 110 Geurts, Ernst, Wehenke (CR15) 2006; 63 Lines, Taylor, Bagnall (CR23) 2018; 12 Altay, Baydoğan (CR1) 2021; 24 Bagnall, Lines, Bostrom (CR2) 2017; 31 Baydoğan, Runger (CR3) 2016; 30 Middlehurst, Bagnall, El Yacoubi, Granger, Yuen (CR27) 2022 Flynn, Large, Bagnall, Pérez García, Sánchez González, Castejón Limas (CR13) 2019 CR6 CR5 CR7 CR28 CR24 Geurts, De Raedt, Siebes (CR14) 2001 Rodríguez, Alonso, Boström, Zighed, Komorowski, Żytkow (CR36) 2000 CR22 CR20 CR40 Lucas, Shifaz, Pelletier (CR26) 2019; 33 Deng, Runger, Tuv (CR12) 2013; 239 Rodríguez, Alonso, Boström (CR37) 2001; 12 Pedregosa, Varoquaux, Gramfort (CR33) 2011; 12 P Geurts (1036_CR15) 2006; 63 JJ Rodríguez (1036_CR37) 2001; 12 1036_CR34 1036_CR11 M Flynn (1036_CR13) 2019 HA Dau (1036_CR8) 2019; 6 1036_CR10 1036_CR32 1036_CR31 CW Tan (1036_CR41) 2022; 36 A Dempster (1036_CR9) 2020; 34 JJ Rodríguez (1036_CR35) 2005 T Altay (1036_CR1) 2021; 24 MG Baydoğan (1036_CR3) 2016; 30 A Guillaume (1036_CR16) 2022 M Middlehurst (1036_CR27) 2022 A Bagnall (1036_CR2) 2017; 31 F Pedregosa (1036_CR33) 2011; 12 JJ Rodríguez (1036_CR36) 2000 MG Baydoğan (1036_CR4) 2013; 35 1036_CR5 M Middlehurst (1036_CR29) 2021 1036_CR7 J Lines (1036_CR23) 2018; 12 1036_CR6 1036_CR19 CH Lubba (1036_CR25) 2019; 33 M Middlehurst (1036_CR30) 2021; 110 1036_CR18 1036_CR17 1036_CR39 1036_CR38 1036_CR24 1036_CR22 1036_CR20 H Deng (1036_CR12) 2013; 239 1036_CR40 H Ismail Fawaz (1036_CR21) 2020; 34 P Geurts (1036_CR14) 2001 B Lucas (1036_CR26) 2019; 33 1036_CR28 |
| References_xml | – volume: 63 start-page: 3 issue: 1 year: 2006 end-page: 42 ident: CR15 article-title: Extremely randomized trees publication-title: Mach Learn doi: 10.1007/s10994-006-6226-1 – ident: CR22 – volume: 110 start-page: 3211 year: 2021 end-page: 3243 ident: CR30 article-title: HIVE-COTE 2.0: a new meta ensemble for time series classification publication-title: Mach Learn doi: 10.1007/s10994-021-06057-9 – volume: 24 start-page: 1490 issue: 6 year: 2021 end-page: 1497 ident: CR1 article-title: A new feature-based time series classification method by using scale-space extrema publication-title: Eng Sci Technol Int J – ident: CR18 – volume: 36 start-page: 1623 issue: 5 year: 2022 end-page: 1646 ident: CR41 article-title: MultiRocket: multiple pooling operators and transformations for fast and effective time series classification publication-title: Data Min Knowl Disc doi: 10.1007/s10618-022-00844-1 – start-page: 115 year: 2001 end-page: 127 ident: CR14 article-title: Pattern extraction for time series classification publication-title: Princip Data Min Knowl Discov doi: 10.1007/3-540-44794-6_10 – start-page: 660 year: 2021 end-page: 676 ident: CR29 article-title: The temporal dictionary ensemble (TDE) classifier for time series classification publication-title: Machine learning and knowledge discovery in databases doi: 10.1007/978-3-030-67658-2_38 – volume: 239 start-page: 142 year: 2013 end-page: 153 ident: CR12 article-title: A time series forest for classification and feature extraction publication-title: Inf Sci doi: 10.1016/j.ins.2013.02.030 – start-page: 653 year: 2022 end-page: 664 ident: CR16 article-title: Random dilated shapelet transform: a new approach for time series shapelets publication-title: Pattern Recognit Artif Intell doi: 10.1007/978-3-031-09037-0_53 – start-page: 244 year: 2005 end-page: 257 ident: CR35 article-title: Support vector machines of interval-based features for time series classification publication-title: Research and development in intelligent systems XXI doi: 10.1007/1-84628-102-4_18 – ident: CR39 – volume: 34 start-page: 1454 issue: 5 year: 2020 end-page: 1495 ident: CR9 article-title: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels publication-title: Data Min Knowl Disc doi: 10.1007/s10618-020-00701-z – volume: 6 start-page: 1293 issue: 6 year: 2019 end-page: 1305 ident: CR8 article-title: The UCR time series archive publication-title: IEEE/CAA J Automatica Sin doi: 10.1109/JAS.2019.1911747 – volume: 35 start-page: 2796 issue: 11 year: 2013 end-page: 2802 ident: CR4 article-title: A bag-of-features framework to classify time series publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.72 – ident: CR10 – ident: CR6 – volume: 12 start-page: 521 issue: 5 year: 2018 end-page: 5235 ident: CR23 article-title: Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles publication-title: ACM Trans Knowl Discov Data doi: 10.1145/3182382 – start-page: 299 year: 2000 end-page: 308 ident: CR36 article-title: Learning first order logic time series classifiers: Rules and boosting publication-title: Principles of data mining and knowledge discovery doi: 10.1007/3-540-45372-5_29 – ident: CR40 – volume: 34 start-page: 1936 issue: 6 year: 2020 end-page: 1962 ident: CR21 article-title: InceptionTime: finding AlexNet for time series classification publication-title: Data Min Knowl Disc doi: 10.1007/s10618-020-00710-y – volume: 33 start-page: 607 issue: 3 year: 2019 end-page: 635 ident: CR26 article-title: Proximity forest: an effective and scalable distance-based classifier for time series publication-title: Data Min Knowl Disc doi: 10.1007/s10618-019-00617-3 – ident: CR19 – volume: 33 start-page: 1821 issue: 6 year: 2019 end-page: 1852 ident: CR25 article-title: catch22: CAnonical time-series characteristics publication-title: Data Min Knowl Disc doi: 10.1007/s10618-019-00647-x – ident: CR38 – ident: CR17 – ident: CR31 – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: CR33 article-title: Scikit-learn: machine learning in python publication-title: J Mach Learn Res – volume: 12 start-page: 245 issue: 3 year: 2001 end-page: 262 ident: CR37 article-title: Boosting interval based literals publication-title: Intell Data Anal doi: 10.3233/IDA-2001-5305 – ident: CR11 – volume: 30 start-page: 476 issue: 2 year: 2016 end-page: 509 ident: CR3 article-title: Time series representation and similarity based on local autopatterns publication-title: Data Min Knowl Disc doi: 10.1007/s10618-015-0425-y – ident: CR32 – ident: CR34 – ident: CR5 – ident: CR7 – volume: 31 start-page: 606 issue: 3 year: 2017 end-page: 660 ident: CR2 article-title: The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances publication-title: Data Min Knowl Disc doi: 10.1007/s10618-016-0483-9 – ident: CR28 – start-page: 381 year: 2019 end-page: 392 ident: CR13 article-title: The contract random interval spectral ensemble (c-RISE): the effect of contracting a classifier on accuracy publication-title: Hybrid Artif Intell Syst doi: 10.1007/978-3-030-29859-3_33 – ident: CR24 – start-page: 150 year: 2022 end-page: 161 ident: CR27 article-title: The FreshPRINCE: a simple transformation based pipeline time series classifier publication-title: Pattern Recognit Artif Intell doi: 10.1007/978-3-031-09282-4_13 – ident: CR20 – ident: 1036_CR17 – volume: 24 start-page: 1490 issue: 6 year: 2021 ident: 1036_CR1 publication-title: Eng Sci Technol Int J – ident: 1036_CR11 doi: 10.1007/s10618-023-00939-3 – ident: 1036_CR7 doi: 10.1007/s10618-023-00978-w – ident: 1036_CR22 doi: 10.1016/j.eswa.2022.118923 – volume: 63 start-page: 3 issue: 1 year: 2006 ident: 1036_CR15 publication-title: Mach Learn doi: 10.1007/s10994-006-6226-1 – volume: 12 start-page: 2825 year: 2011 ident: 1036_CR33 publication-title: J Mach Learn Res – volume: 12 start-page: 245 issue: 3 year: 2001 ident: 1036_CR37 publication-title: Intell Data Anal doi: 10.3233/IDA-2001-5305 – start-page: 381 volume-title: Hybrid Artif Intell Syst year: 2019 ident: 1036_CR13 doi: 10.1007/978-3-030-29859-3_33 – volume: 31 start-page: 606 issue: 3 year: 2017 ident: 1036_CR2 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-016-0483-9 – ident: 1036_CR40 doi: 10.58895/ksp/1000138532-7 – volume: 34 start-page: 1936 issue: 6 year: 2020 ident: 1036_CR21 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-020-00710-y – ident: 1036_CR32 doi: 10.1007/s10618-024-01022-1 – start-page: 660 volume-title: Machine learning and knowledge discovery in databases year: 2021 ident: 1036_CR29 doi: 10.1007/978-3-030-67658-2_38 – start-page: 244 volume-title: Research and development in intelligent systems XXI year: 2005 ident: 1036_CR35 doi: 10.1007/1-84628-102-4_18 – ident: 1036_CR28 doi: 10.1109/BigData50022.2020.9378424 – start-page: 299 volume-title: Principles of data mining and knowledge discovery year: 2000 ident: 1036_CR36 doi: 10.1007/3-540-45372-5_29 – ident: 1036_CR5 doi: 10.1109/ICDM51629.2021.00113 – ident: 1036_CR18 – start-page: 115 volume-title: Princip Data Min Knowl Discov year: 2001 ident: 1036_CR14 doi: 10.1007/3-540-44794-6_10 – ident: 1036_CR39 doi: 10.1007/s10994-023-06395-w – volume: 30 start-page: 476 issue: 2 year: 2016 ident: 1036_CR3 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-015-0425-y – volume: 34 start-page: 1454 issue: 5 year: 2020 ident: 1036_CR9 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-020-00701-z – ident: 1036_CR20 – volume: 239 start-page: 142 year: 2013 ident: 1036_CR12 publication-title: Inf Sci doi: 10.1016/j.ins.2013.02.030 – ident: 1036_CR24 – volume: 33 start-page: 1821 issue: 6 year: 2019 ident: 1036_CR25 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-019-00647-x – ident: 1036_CR31 doi: 10.1007/s10618-024-01022-1 – volume: 12 start-page: 521 issue: 5 year: 2018 ident: 1036_CR23 publication-title: ACM Trans Knowl Discov Data doi: 10.1145/3182382 – volume: 35 start-page: 2796 issue: 11 year: 2013 ident: 1036_CR4 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.72 – start-page: 653 volume-title: Pattern Recognit Artif Intell year: 2022 ident: 1036_CR16 doi: 10.1007/978-3-031-09037-0_53 – ident: 1036_CR38 doi: 10.1145/3132847.3132980 – ident: 1036_CR19 doi: 10.1109/BigData55660.2022.10020496 – ident: 1036_CR34 doi: 10.1145/967900.968015 – volume: 6 start-page: 1293 issue: 6 year: 2019 ident: 1036_CR8 publication-title: IEEE/CAA J Automatica Sin doi: 10.1109/JAS.2019.1911747 – ident: 1036_CR6 doi: 10.1109/ICDM50108.2020.00107 – volume: 33 start-page: 607 issue: 3 year: 2019 ident: 1036_CR26 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-019-00617-3 – volume: 36 start-page: 1623 issue: 5 year: 2022 ident: 1036_CR41 publication-title: Data Min Knowl Disc doi: 10.1007/s10618-022-00844-1 – start-page: 150 volume-title: Pattern Recognit Artif Intell year: 2022 ident: 1036_CR27 doi: 10.1007/978-3-031-09282-4_13 – ident: 1036_CR10 doi: 10.1145/3447548.3467231 – volume: 110 start-page: 3211 year: 2021 ident: 1036_CR30 publication-title: Mach Learn doi: 10.1007/s10994-021-06057-9 |
| SSID | ssj0005230 |
| Score | 2.459134 |
| Snippet | We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2377 |
| SubjectTerms | Accuracy Archives & records Artificial Intelligence Chemistry and Earth Sciences Classification Computer Science Data Mining and Knowledge Discovery Datasets Decision trees Feature selection Fourier transforms Information Storage and Retrieval Methods Physics Statistics for Engineering Time series |
| SummonAdditionalLinks | – databaseName: SpringerLink Journals (ICM) dbid: U2A link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB60InjxURWrVfbgTRe62Uc23lQsRdCThd5Csw8QtD6S_n9nN4mtooLnbOYwj8w32Zn5AE6N8ZKn0tNCO06F5AXNhLE0SQ03VgTqtPBr4O5ejcbidiInzVBY2Xa7t1eS8Uu9NOymmKaYU2jgJlA0W4U1GWp39OJxcrnU2MHr2WAtqNRs0IzK_CzjazpaYMxv16Ix2wy3YbOBieSytusOrLhZF7ZaCgbSRGQX1mMHpyl34eptjkq6IFMStoU8h72GFXmMHY0oqCaKJohQSWCTJ8HxXElMwM6hWSjaZw_Gw5uH6xFtCBKo4YpXtFBMDYx2yttp4rRwU5YUUxOqoBQjjXNhpbAcMZvzoVIqhBgYy1ThLDeZ8HwfOrOXmTsAIhC3MmMRXnGPCElrJpxO0c28YCb1sges1VNumu3hgcTiKV_sPQ66zVG3edRtnvXg7POd13p3xp-n-6368yaOypxjxYU1HNNpD85bkywe_y7t8H_Hj2AjiV4R-nD70Kne5-4Y0UZVnETn-gDwRci- priority: 102 providerName: Springer Nature |
| Title | quant: a minimalist interval method for time series classification |
| URI | https://link.springer.com/article/10.1007/s10618-024-01036-9 https://www.proquest.com/docview/3086149187 |
| Volume | 38 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NTwIxEJ0oXLz4bUSR9OBNG-m22y1eDBKQaCTGSKKnDdt2ExMFFPz_zixdURO97GF328ObtvOmnc4DOLY2j2US5zwzXnIVy4y3lHU8Sqy0TpF0Gm0N3A50f6iuH-PHsOE2C2mV5ZpYLNRuYmmP_Ewi90Y2L0xyMX3jpBpFp6tBQmMVqrgEGwy-qpfdwd39tyQPubgnbBSPjWiGazPh8pwWhqOP4qR1oHnrp2ta8s1fR6SF5-ltwnqgjKy9sPEWrPjxNmyUcgwszM4duHz7QJjO2YhRvZBXqmw4Z89FTiM2X0hFM-SojPTkGQ09P2OW2DOlCxUW2oVhr_vQ6fMgkcCt1HLOMy100xqvczeKvFF-JKJsZCkOSnCuSalcrJxE1uZzipUypZrWCZ15J21L5XIPKuPJ2O8DU8hchXVIsGSOHMkYobxJcKDlStgkj2sgSnRSG-qHk4zFS7qsfEyIpohoWiCatmpw8tVmuqie8e_f9RL0NMykWbq0ew1OS0MsP__d28H_vR3CWlTYnjJv61CZv3_4I-QX86wBq6Z31YBq--rpptsIQwrfdnQHn8Oo_QlXs86O |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JTsMwEB1BOcCFHVEo4AOcwKKOncRBQoittGwnkLiFxnYkJCiUtkL8FN_ITBYKSHDrObEP42fPG3tmHsCmMakvQz_liXaSK18mPFLGci800lhF0ml0NXB1HTRv1fmdfzcGH2UtDKVVlmdidlDbZ0N35LsSuTeyeaHDg5cuJ9Uoel0tJTRyWFy49zcM2Xr7rRNc3y3Pa5zeHDd5oSrAjQxknyeBCOpGuyC1bc9p5drCS9qGQocQ4Smlsr6yEomOSym8SJSqGyuCxFlpIpVKnHccJpSUEe0o3Tj7llIi86pkrbivRb0o0ilK9QKhOXpETsoKAY9-OsIhu_31IJv5ucYsTBcElR3miJqDMdeZh5lS_IEVZ8ECHHUHuCh7rM2oO8kT9VHss4csgxKH58LUDBkxI_V6RkB3PWaIq1NyUoaHRbgdiemWoNJ57rhlYAp5sjAW6ZxMkZFpLZTTIcI6VcKEqV8FUVonNkW3chLNeIyHfZbJojFaNM4sGkdV2P4a85L36vj371pp9LjYt714iLIq7JQLMfz892wr_8-2AZPNm6vL-LJ1fbEKU16GA8r5rUGl_zpwa8hs-sl6BicG96PG7ycoEgWT |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LTxsxEB7RIFW9FOhDpFDwoZzAIl57dx2kquKRiEcbRahI3LZZPyQkmkeTCPHX-HWd2fUSQCK3nHc9h_Fn-xt7Zj6Ab8b4WKax57l2kqtY5rypjOVRaqSxiqTT6GrgVyc5vVLn1_H1EjxUtTCUVlnticVGbQeG7sj3JXJvZPNCp_s-pEV0T9o_hiNOClL00lrJaZQQuXD3dxi-jb-fneBc70RRu_X7-JQHhQFuZCInPE9E0jDaJd72IqeV64ko7xkKI1KEqpTKxspKJD3OU6iRK9UwViS5s9I0lZdo9w0spxgVNWqwfNTqdC-fJJjIskZZKx5r0QglO6FwLxGa4_nISWch4c3nx-KM6754ni1OvfYqvA90lR2W-FqDJdf_ACuVFAQLO8NHOBpNcYoOWI9Rr5K_1FVxwm6KfEocXspUM-THjLTsGcHejZkh5k6pSgU6PsHVQpz3GWr9Qd-tA1PImoWxSO6kR36mtVBOpwhyr4RJfVwHUXknM6F3OUlo3Gazrsvk0Qw9mhUezZp12H0cMyw7d8z9e7NyehZW8TibYa4Oe9VEzD6_bu3LfGvb8Baxm_0861xswLuogAElAG9CbfJv6r4izZnkWwFPDP4sGsL_AeM9CyU |
| 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=quant%3A+a+minimalist+interval+method+for+time+series+classification&rft.jtitle=Data+mining+and+knowledge+discovery&rft.au=Dempster%2C+Angus&rft.au=Schmidt%2C+Daniel+F&rft.au=Webb%2C+Geoffrey+I&rft.date=2024-07-01&rft.pub=Springer+Nature+B.V&rft.issn=1384-5810&rft.eissn=1573-756X&rft.volume=38&rft.issue=4&rft.spage=2377&rft.epage=2402&rft_id=info:doi/10.1007%2Fs10618-024-01036-9&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1384-5810&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1384-5810&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1384-5810&client=summon |