F4: An All-Purpose Tool for Multivariate Time Series Classification
We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the...
Saved in:
| Published in | Mathematics (Basel) Vol. 9; no. 23; p. 3051 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.12.2021
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2227-7390 2227-7390 |
| DOI | 10.3390/math9233051 |
Cover
| Abstract | We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive. |
|---|---|
| AbstractList | We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive. |
| Author | López-Oriona, Ángel Vilar, José A. |
| Author_xml | – sequence: 1 givenname: Ángel orcidid: 0000-0003-1456-7342 surname: López-Oriona fullname: López-Oriona, Ángel – sequence: 2 givenname: José A. orcidid: 0000-0001-5494-171X surname: Vilar fullname: Vilar, José A. |
| BookMark | eNqFkFtLBCEYhiUKOl71Bwa6rCkPM6N2tyydoCioruVbV8vFHTd1iv33WRsREeSN-vJ8D3zvNlrvQ28Q2if4mDGJT-aQnyVlDLdkDW1RSnnNS77-472J9lKa4XIkYaKRW2h83pxWo74aeV_fDXERkqkeQvCVDbG6GXx2rxAd5JK6uanuTXQmVWMPKTnrNGQX-l20YcEns_d176DH87OH8WV9fXtxNR5d15p1Ta4laGkN6QThGPiEs9ZgYaayaxmGlrad0IRzOmkEnWqYYCt1y2wjWcu1JsayHXS18k4DzNQiujnEpQrg1GcQ4pOCmJ32RlHATFjoilE2tAEpOs4aPOWY8fIxxXW0cg39ApZv4P23kGD10af60WfBD1b4IoaXwaSsZmGIfdlW0Q4LwqhguFCHK0rHkFI09h8n-UVrlz8LzRGc_3PmHWQqkpc |
| CitedBy_id | crossref_primary_10_1016_j_fss_2022_02_015 crossref_primary_10_1016_j_ijar_2022_07_010 crossref_primary_10_1016_j_ins_2022_10_048 crossref_primary_10_1016_j_neucom_2023_02_048 |
| Cites_doi | 10.1016/j.qref.2006.05.006 10.1029/98WR01449 10.1109/ACCESS.2017.2779939 10.1161/01.CIR.101.23.e215 10.1016/0304-4076(95)01753-4 10.1088/1757-899X/536/1/012003 10.1007/s10618-014-0349-y 10.1002/widm.1301 10.1016/j.eswa.2014.11.007 10.1145/3132847.3132980 10.1007/s11634-015-0208-8 10.1023/A:1010933404324 10.1016/j.compbiomed.2014.08.010 10.1109/ICSMB.2010.5735345 10.15439/2015F419 10.1142/9789812836281_0002 10.1016/j.mri.2008.01.052 10.1080/13504850500447331 10.1007/978-3-642-59471-7_12 10.1109/IJCNN48605.2020.9206751 10.1007/s00357-014-9163-x 10.1016/j.fss.2011.10.002 10.1016/j.patcog.2010.09.022 10.1007/s10115-005-0223-8 10.1109/SSCI.2015.199 10.1137/1.9781611972757.4 10.1016/j.protcy.2016.05.195 10.1093/ectj/utz002 10.1111/j.1467-937X.2005.00353.x 10.1109/TCYB.2015.2426723 10.1109/10.661153 10.1016/S0305-0548(99)00123-9 10.1080/01621459.1998.10474114 10.1109/ICCCBDA.2018.8386483 10.1109/TIM.2018.2816458 10.1016/j.asieco.2007.03.007 10.1016/j.csda.2013.09.006 10.1007/s10618-015-0418-x 10.1080/09603100601057854 10.17678/beuscitech.344953 10.1145/3182382 10.1016/j.pneurobio.2005.10.003 10.1109/TIE.2018.2864702 10.1109/ICHI.2018.00092 10.1007/s10618-016-0455-0 10.1198/073500102288618487 10.1109/TIM.2013.2279001 10.1016/j.neunet.2019.04.014 10.1016/j.eswa.2021.115677 10.1214/aos/1013203451 10.5539/ijef.v2n3p85 10.1007/s00186-008-0247-4 10.1016/j.knosys.2008.03.027 10.1017/CBO9781139540933 10.1016/j.eswa.2012.05.012 10.1002/jae.842 10.1007/978-1-84628-799-2_7 10.1007/s10687-016-0251-7 |
| ContentType | Journal Article |
| Copyright | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 3V. 7SC 7TB 7XB 8AL 8FD 8FE 8FG 8FK ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO FR3 GNUQQ HCIFZ JQ2 K7- KR7 L6V L7M L~C L~D M0N M7S P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U ADTOC UNPAY DOA |
| DOI | 10.3390/math9233051 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Engineering Research Database ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database (Proquest) Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Engineering Database (Proquest) ProQuest Advanced Technologies & Aerospace Collection Proquest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts 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 ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Advanced Technologies & Aerospace Collection Civil Engineering Abstracts ProQuest Computing Engineering Database ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 2227-7390 |
| ExternalDocumentID | oai_doaj_org_article_2a038fa6c179424a9867340d7037a98e 10.3390/math9233051 10_3390_math9233051 |
| GroupedDBID | -~X 5VS 85S 8FE 8FG AADQD AAFWJ AAYXX ABDBF ABJCF ABPPZ ABUWG ACIPV ACIWK ADBBV AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS AMVHM ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO GNUQQ GROUPED_DOAJ HCIFZ IAO ITC K6V K7- KQ8 L6V M7S MODMG M~E OK1 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS RNS 3V. 7SC 7TB 7XB 8AL 8FD 8FK FR3 JQ2 KR7 L7M L~C L~D M0N P62 PKEHL PQEST PQUKI PRINS Q9U ADTOC IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c364t-9ac9fe168170a7b735e08ed96530a52568c1772b482dcab0f9c53f49357cc1ef3 |
| IEDL.DBID | DOA |
| ISSN | 2227-7390 |
| IngestDate | Fri Oct 03 12:40:49 EDT 2025 Sun Oct 26 04:05:35 EDT 2025 Fri Jul 25 12:00:28 EDT 2025 Thu Apr 24 23:06:01 EDT 2025 Thu Oct 16 04:29:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 23 |
| Language | English |
| License | cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c364t-9ac9fe168170a7b735e08ed96530a52568c1772b482dcab0f9c53f49357cc1ef3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5494-171X 0000-0003-1456-7342 |
| OpenAccessLink | https://doaj.org/article/2a038fa6c179424a9867340d7037a98e |
| PQID | 2608132830 |
| PQPubID | 2032364 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2a038fa6c179424a9867340d7037a98e unpaywall_primary_10_3390_math9233051 proquest_journals_2608132830 crossref_primary_10_3390_math9233051 crossref_citationtrail_10_3390_math9233051 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-01 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Mathematics (Basel) |
| PublicationYear | 2021 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Weron (ref_53) 2009; 69 ref_14 ref_56 Durso (ref_32) 2012; 193 ref_55 Probst (ref_50) 2019; 9 ref_51 Baydogan (ref_18) 2014; 29 ref_19 ref_17 ref_16 ref_59 Hu (ref_10) 2017; 31 Pereda (ref_42) 2005; 77 Primiceri (ref_58) 2005; 72 Bauwens (ref_45) 2006; 21 Engle (ref_44) 2002; 20 ref_24 Jeong (ref_8) 2011; 44 ref_23 Andersson (ref_49) 2008; 18 ref_66 Mei (ref_11) 2015; 46 Kley (ref_33) 2019; 22 Ku (ref_46) 2007; 14 Alonso (ref_57) 2014; 31 Liu (ref_61) 2015; 61 ref_20 ref_64 Maharaj (ref_26) 2014; 70 Davis (ref_54) 2016; 19 Li (ref_4) 2006; 10 Hassan (ref_29) 2007; 47 ref_28 Liu (ref_22) 2018; 66 Weng (ref_6) 2008; 21 Abonyi (ref_12) 2012; 39 Naoui (ref_47) 2010; 2 Kakizawa (ref_3) 1998; 93 Karim (ref_15) 2019; 116 (ref_13) 2015; 42 ref_34 Zhang (ref_37) 2001; 28 Kate (ref_9) 2016; 30 Vilar (ref_31) 2021; 185 Meina (ref_25) 2015; 5 Karim (ref_21) 2017; 6 Friedman (ref_36) 2001; 29 Breiman (ref_35) 2001; 45 ref_39 Anderson (ref_30) 1998; 45 Handhika (ref_2) 2019; Volume 536 Goldberger (ref_63) 2000; 101 Kuper (ref_48) 2007; 18 Anderson (ref_52) 1998; 34 Diker (ref_60) 2017; 7 ref_41 Remya (ref_65) 2016; 24 ref_40 Sadhukhan (ref_62) 2018; 67 ref_1 Banerjee (ref_67) 2013; 63 Formisano (ref_27) 2008; 26 Koop (ref_43) 1996; 74 Vilar (ref_38) 2016; 10 ref_5 ref_7 |
| References_xml | – volume: 47 start-page: 470 year: 2007 ident: ref_29 article-title: Multivariate GARCH modeling of sector volatility transmission publication-title: Q. Rev. Econ. Financ. doi: 10.1016/j.qref.2006.05.006 – volume: 34 start-page: 2271 year: 1998 ident: ref_52 article-title: Modeling river flows with heavy tails publication-title: Water Resour. Res. doi: 10.1029/98WR01449 – ident: ref_55 – volume: 6 start-page: 1662 year: 2017 ident: ref_21 article-title: LSTM Fully Convolutional Networks for Time Series Classification publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2779939 – volume: 101 start-page: e215 year: 2000 ident: ref_63 article-title: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – volume: 74 start-page: 119 year: 1996 ident: ref_43 article-title: Impulse response analysis in nonlinear multivariate models publication-title: J. Econom. doi: 10.1016/0304-4076(95)01753-4 – volume: Volume 536 start-page: 012003 year: 2019 ident: ref_2 article-title: Multivariate time series classification analysis: State-of-the-art and future challenges publication-title: IOP Conference Series: Materials Science and Engineering, Proceedings of the International Conference on Science and Innovated Engineering (I-COSINE), Aceh, Indonesia, 21–22 October 2018 doi: 10.1088/1757-899X/536/1/012003 – volume: 29 start-page: 400 year: 2014 ident: ref_18 article-title: Learning a symbolic representation for multivariate time series classification publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-014-0349-y – volume: 9 start-page: e1301 year: 2019 ident: ref_50 article-title: Hyperparameters and tuning strategies for random forest publication-title: Wiley Interdiscip. Rev. Data Min. Knowl. Discov. doi: 10.1002/widm.1301 – ident: ref_39 – volume: 42 start-page: 2305 year: 2015 ident: ref_13 article-title: Multivariate time series classification with parametric derivative dynamic time warping publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.11.007 – ident: ref_16 doi: 10.1145/3132847.3132980 – volume: 10 start-page: 391 year: 2016 ident: ref_38 article-title: Clustering of time series using quantile autocovariances publication-title: Adv. Data Anal. Classif. doi: 10.1007/s11634-015-0208-8 – ident: ref_17 doi: 10.1145/3132847.3132980 – volume: 45 start-page: 5 year: 2001 ident: ref_35 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 61 start-page: 178 year: 2015 ident: ref_61 article-title: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2014.08.010 – ident: ref_66 doi: 10.1109/ICSMB.2010.5735345 – ident: ref_24 doi: 10.15439/2015F419 – ident: ref_41 doi: 10.1142/9789812836281_0002 – volume: 26 start-page: 921 year: 2008 ident: ref_27 article-title: Multivariate analysis of fMRI time series: Classification and regression of brain responses using machine learning publication-title: Magn. Reson. Imaging doi: 10.1016/j.mri.2008.01.052 – ident: ref_56 – volume: 14 start-page: 503 year: 2007 ident: ref_46 article-title: On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios publication-title: Appl. Econ. Lett. doi: 10.1080/13504850500447331 – ident: ref_34 doi: 10.1007/978-3-642-59471-7_12 – ident: ref_23 doi: 10.1109/IJCNN48605.2020.9206751 – volume: 31 start-page: 325 year: 2014 ident: ref_57 article-title: Robust functional supervised classification for time series publication-title: J. Classif. doi: 10.1007/s00357-014-9163-x – volume: 193 start-page: 33 year: 2012 ident: ref_32 article-title: Wavelets-based clustering of multivariate time series publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2011.10.002 – volume: 44 start-page: 2231 year: 2011 ident: ref_8 article-title: Weighted dynamic time warping for time series classification publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2010.09.022 – volume: 10 start-page: 163 year: 2006 ident: ref_4 article-title: Real-time classification of variable length multi-attribute motions publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-005-0223-8 – ident: ref_28 doi: 10.1109/SSCI.2015.199 – ident: ref_7 doi: 10.1137/1.9781611972757.4 – volume: 24 start-page: 949 year: 2016 ident: ref_65 article-title: Classification of myocardial infarction using multi resolution wavelet analysis of ECG publication-title: Procedia Technol. doi: 10.1016/j.protcy.2016.05.195 – ident: ref_20 – volume: 22 start-page: 131 year: 2019 ident: ref_33 article-title: Quantile coherency: A general measure for dependence between cyclical economic variables publication-title: Econom. J. doi: 10.1093/ectj/utz002 – volume: 72 start-page: 821 year: 2005 ident: ref_58 article-title: Time varying structural vector autoregressions and monetary policy publication-title: Rev. Econ. Stud. doi: 10.1111/j.1467-937X.2005.00353.x – ident: ref_59 – volume: 46 start-page: 1363 year: 2015 ident: ref_11 article-title: Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2015.2426723 – volume: 45 start-page: 277 year: 1998 ident: ref_30 article-title: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/10.661153 – volume: 28 start-page: 381 year: 2001 ident: ref_37 article-title: A simulation study of artificial neural networks for nonlinear time-series forecasting publication-title: Comput. Oper. Res. doi: 10.1016/S0305-0548(99)00123-9 – volume: 93 start-page: 328 year: 1998 ident: ref_3 article-title: Discrimination and clustering for multivariate time series publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1998.10474114 – ident: ref_1 doi: 10.1109/ICCCBDA.2018.8386483 – volume: 67 start-page: 2303 year: 2018 ident: ref_62 article-title: Automated identification of myocardial infarction using harmonic phase distribution pattern of ECG data publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2018.2816458 – volume: 18 start-page: 670 year: 2007 ident: ref_48 article-title: Dynamic conditional correlation analysis of financial market interdependence: An application to Thailand and Indonesia publication-title: J. Asian Econ. doi: 10.1016/j.asieco.2007.03.007 – volume: 70 start-page: 67 year: 2014 ident: ref_26 article-title: Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2013.09.006 – volume: 30 start-page: 283 year: 2016 ident: ref_9 article-title: Using dynamic time warping distances as features for improved time series classification publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-015-0418-x – volume: 18 start-page: 139 year: 2008 ident: ref_49 article-title: Why does the correlation between stock and bond returns vary over time? publication-title: Appl. Financ. Econ. doi: 10.1080/09603100601057854 – ident: ref_40 – volume: 7 start-page: 132 year: 2017 ident: ref_60 article-title: A diagnostic model for identification of myocardial infarction from electrocardiography signals publication-title: Bitlis Eren Univ. J. Sci. Technol. doi: 10.17678/beuscitech.344953 – ident: ref_14 – volume: 5 start-page: 367 year: 2015 ident: ref_25 article-title: Tagging Firefighter Activities at the Emergency Scene: Summary of AAIA’15 Data Mining Competition at Knowledge Pit publication-title: Ann. Comput. Sci. Inf. Syst. – ident: ref_19 doi: 10.1145/3182382 – volume: 77 start-page: 1 year: 2005 ident: ref_42 article-title: Nonlinear multivariate analysis of neurophysiological signals publication-title: Prog. Neurobiol. doi: 10.1016/j.pneurobio.2005.10.003 – volume: 66 start-page: 4788 year: 2018 ident: ref_22 article-title: Time series classification with multivariate convolutional neural network publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2018.2864702 – ident: ref_64 doi: 10.1109/ICHI.2018.00092 – volume: 31 start-page: 1 year: 2017 ident: ref_10 article-title: Generalizing DTW to the multi-dimensional case requires an adaptive approach publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-016-0455-0 – volume: 20 start-page: 339 year: 2002 ident: ref_44 article-title: Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models publication-title: J. Bus. Econ. Stat. doi: 10.1198/073500102288618487 – volume: 63 start-page: 326 year: 2013 ident: ref_67 article-title: Application of cross wavelet transform for ECG pattern analysis and classification publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2013.2279001 – volume: 116 start-page: 237 year: 2019 ident: ref_15 article-title: Multivariate LSTM-FCNs for time series classification publication-title: Neural Netw. doi: 10.1016/j.neunet.2019.04.014 – volume: 185 start-page: 115677 year: 2021 ident: ref_31 article-title: Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115677 – volume: 29 start-page: 1189 year: 2001 ident: ref_36 article-title: Greedy function approximation: A gradient boosting machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – volume: 2 start-page: 85 year: 2010 ident: ref_47 article-title: A dynamic conditional correlation analysis of financial contagion: The case of the subprime credit crisis publication-title: Int. J. Econ. Financ. doi: 10.5539/ijef.v2n3p85 – volume: 69 start-page: 457 year: 2009 ident: ref_53 article-title: Heavy-tails and regime-switching in electricity prices publication-title: Math. Methods Oper. Res. doi: 10.1007/s00186-008-0247-4 – volume: 21 start-page: 581 year: 2008 ident: ref_6 article-title: Classification of multivariate time series using locality preserving projections publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2008.03.027 – ident: ref_51 doi: 10.1017/CBO9781139540933 – volume: 39 start-page: 12814 year: 2012 ident: ref_12 article-title: Correlation based dynamic time warping of multivariate time series publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.05.012 – volume: 21 start-page: 79 year: 2006 ident: ref_45 article-title: Multivariate GARCH models: A survey publication-title: J. Appl. Econom. doi: 10.1002/jae.842 – ident: ref_5 doi: 10.1007/978-1-84628-799-2_7 – volume: 19 start-page: 517 year: 2016 ident: ref_54 article-title: Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series publication-title: Extremes doi: 10.1007/s10687-016-0251-7 |
| SSID | ssj0000913849 |
| Score | 2.2032793 |
| Snippet | We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying... |
| SourceID | doaj unpaywall proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 3051 |
| SubjectTerms | Algorithms Classification Datasets Deep learning Discrete Wavelet Transform Discriminant analysis ECG signals Electrocardiography Feature extraction Feature selection Internet of Things Mathematics Multivariate analysis multivariate time series Neural networks quantile analysis random forest Support vector machines Time series wavelet analysis Wavelet transforms |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Na9wwEB3SzaHtIfSTbpMWHdJLQcS2JEsqhLAJWUIhSygJ5GZkWW4Pxt5mNwn9953R2m4CJUcbIcyMNHojz7wHsJ_ZWujSB56ooLnUxnKXKMW9K1WZOV2LKNN5vsjPruT3a3W9BYuhF4bKKoeYGAN11Xm6Iz9A3G0wczIiOVr-5qQaRX9XBwkN10srVIeRYuwZbGfEjDWB7ePTxcWP8daFWDCNtJtGPYH5_gHiwl8IcnDZp4-Opsjg_wh2Pr9tl-7PvWuaByfQ_BXs9NCRzTa-fg1boX0DL89H3tXVWziZy29s1rJZ0_ALNGG3Cuyy6xqG0JTFXts7zI0RXjJq_WB0NRZWLApjUslQ9NI7uJqfXp6c8V4mgXuRyzW3zts6pDlR7TldaqFCYkJlcyUSpxDSGJ8ihi6lySp0QVJbr0QtrVDa-zTU4j1M2q4NH4A5mafGZ1JjoiGVJb7EJChLJJIyLbWewtfBQoXvOcRJyqIpMJcgcxYPzDmF_XHwckOd8f9hx2TqcQjxXccX3c3Pot8-ReYSYWqXe4ofmXTW5FrIpMJ4pfEhTGFvcFTRb8JV8W_JTOHL6LynvuXj09PswouMSlpiNcseTNY3t-ETYpJ1-blfaH8BeO3d8A priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Na9wwEBVlc2h7SNMvumlSdEgvBWVlfVhSL2UTsoRCQg5ZSE-urJXTUmMv8W5K--s749UuSSml0KPNWMi8kfRGzLwh5EC4SpoyRMZ1NEwZ65jnWrPgS10KbyrZt-k8O89Pp-rjlb5KfU67lFYJofjXfpPGOk1mICofuZGQI3DNbDSfVR9u01UScgk4fgXqgW7lGsj4gGxNzy_Gn7Cl3PrjVVWexGGABH4BRoMD3TuHern-exzz4bKZ-x_ffV3fOW4mT8jn9URXWSbfDpeL8jD8_E3D8T_-ZIdsJypKxyvfeUoexOYZeXy20XHtnpPjiXpPxw0d1zW7AEjaLtLLtq0pUF3a1-7eQqwNdJViKQnFq7bY0b7RJqYg9ai_INPJyeXxKUttF1iQuVow54OrYpajdJ83pZE6chtnLteSew0UyYYMOHmprJgBpLxyQctKOalNCFms5EsyaNomviLUqzyzQSgDgYvSDvUXedQORSlVVhozJO_WIBQhaZJja4y6gNgEESvuIDYkBxvj-UqK489mR4jmxgT1s_sX7c11kZZjITyXtvJ5wP1IKO9sbqTiM9j_DDzEIdlb-0KRFnVXQOhnIXi3kg_J241__G0uu_9o95o8Epgr06fJ7JHB4mYZ94HsLMo3yaF_AXWI9Zs priority: 102 providerName: Unpaywall |
| Title | F4: An All-Purpose Tool for Multivariate Time Series Classification |
| URI | https://www.proquest.com/docview/2608132830 https://www.mdpi.com/2227-7390/9/23/3051/pdf?version=1638273204 https://doaj.org/article/2a038fa6c179424a9867340d7037a98e |
| UnpaywallVersion | publishedVersion |
| Volume | 9 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: KQ8 dateStart: 20130101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: ABDBF dateStart: 20170101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Mathematics Source customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: AMVHM dateStart: 20170101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/mathematics-source providerName: EBSCOhost – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 2227-7390 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000913849 issn: 2227-7390 databaseCode: 8FG dateStart: 20130301 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NSwMxEB38OKgH8RPrR8lBL8JidpNsEm9VrCJYiljQ05JNs3hYtsVWxX_vJLuWCqIXj1lyCG8myXvL5A3AcaILJnPrIiqcjLhUOjJUiMiaXOSJkQULbTrveunNgN8-ise5Vl--Jqy2B66BO0sMZaowqfWZk3CjVSoZp0PMVIkD509fqvScmApnsI6Z4rp-kMdQ158h_3tGMoPpHX-7goJT_zd6ufJajc3HuynLuZumuwHrDUUknXppm7Dgqi1Yu5v5q0624bLLz0mnIp2yjPoI1WjiyMNoVBKkoCS8qX1DDYw0kvgnHsT_AnMTEhpg-tKgEI0dGHSvHi5voqYdQmRZyqeRNlYXLk69pZ6RuWTCUeWGOhWMGoHURSFGMsm5SoYINS20FazgmglpbewKtgtL1ahye0AMT2NlEy5RUHChvS8idUJ7s0ge51K24PQLocw2XuG-ZUWZoWbwcGZzcLbgeDZ5XFtk_DztwkM9m-J9rcMHjHbWRDv7K9otOPwKVNZstkmGkkyhqFaMtuBkFrzf1rL_H2s5gNXEF7iE2pZDWJq-vLojZCjTvA2LqnvdhuWLq17_vh1SE0eDXr_z9AlaOeLb |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NT9wwEB1RONAeqn6KbSn1AS6VIpzYju1KqFo-VkthV6haJG6p4zjtIUq2ZCniz_W3dZxNAkiIG8dElpXMjJ03zsx7ANuRzplMrQuocDLgUunAUCECa1KRRkbmrJHpnEzj8Tn_fiEuVuBf1wvjyyq7PbHZqLPK-jPyXcTdCjMnxei3-Z_Aq0b5v6udhIZppRWyvYZirG3sOHE315jC1XvHh-jvnSgaHc0OxkGrMhBYFvNFoI3VuQtjz1RnZCqZcFS5TMeCUSMQESgbIgRNuYoyfAOaaytYzjUT0trQ5QznfQZrnHGNyd_a_tH07Ed_yuNZNxXXy8ZAxjTdRRz6G0EVLrPw3qewUQy4B3PXr8q5ubk2RXHnizd6BS9bqEqGy9h6DSuufAMvJj3Pa_0WDkb8KxmWZFgUwRm6rKodmVVVQRAKk6a39y_m4ghniW81If4oztWkEeL0JUpNVLyD8ycx2HtYLavSbQAxPA6VjbjExIYL7fkZqRPak1byMJVyAF86CyW25Sz30hlFgrmLN2dyx5wD2O4Hz5dUHQ8P2_em7od4fu3mRnX5K2mXaxIZylRuYuv3q4gbrWLJOM1wf5R44Qaw2TkqaRd9ndyG6AB2euc99iwfHp_mM6yPZ5PT5PR4evIRnke-nKappNmE1cXllfuEeGiRbrVBR-DnU8f5f7b5Gn8 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwEB2VIgE9IL4qFgr40F6QonViO7aREFpaQktp1UMr9RYcrwOHKFmabav-NX4dM9lkaSXUW4-xLMuaGY_fODNvADYTWwpd-BBxFXQktbGR40pF3hWqSJwuRdem8-Aw3T2R307V6Qr8GWphKK1y8Imdo542nt7Ix4i7DUZORvBx2adFHO1kn2a_I-ogRX9ah3YaCxPZD1eXGL61H_d2UNdbSZJ9Od7ejfoOA5EXqZxH1nlbhjglljqnCy1U4CZMbaoEdwrRgPExws9CmmSKu-el9UqU0gqlvY9DKXDde3BfE4s7ValnX5fvO8S3aaRdlAQKYfkYEegvhFN4wOIbl2DXK-AGwH14Xs_c1aWrqmt3XfYEHvcglU0WVvUUVkL9DNYOlgyv7XPYzuQHNqnZpKqiI1RW0wZ23DQVQxDMuqreC4zCEcgyKjJh9AgXWta14KTkpM4eXsDJnYhrHVbrpg4vgTmZxsYnUmNII5UlZkYelCW6ShkXWo_g_SCh3Pds5dQ0o8oxaiFx5tfEOYLN5eTZgqTj_9M-k6iXU4hZuxtozn7m_UHNE8eFKV3qyVMl0lmTaiH5FD2jxo8wgo1BUXl_3Nv8n3GOYGupvNv28ur2Zd7BA7Tu_Pve4f5reJRQHk2XQrMBq_Oz8_AGgdC8eNtZHIMfd23ifwHynBgZ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Na9wwEBVlc2h7SNMvumlSdEgvBWVlfVhSL2UTsoRCQg5ZSE-urJXTUmMv8W5K--s749UuSSml0KPNWMi8kfRGzLwh5EC4SpoyRMZ1NEwZ65jnWrPgS10KbyrZt-k8O89Pp-rjlb5KfU67lFYJofjXfpPGOk1mICofuZGQI3DNbDSfVR9u01UScgk4fgXqgW7lGsj4gGxNzy_Gn7Cl3PrjVVWexGGABH4BRoMD3TuHern-exzz4bKZ-x_ffV3fOW4mT8jn9URXWSbfDpeL8jD8_E3D8T_-ZIdsJypKxyvfeUoexOYZeXy20XHtnpPjiXpPxw0d1zW7AEjaLtLLtq0pUF3a1-7eQqwNdJViKQnFq7bY0b7RJqYg9ai_INPJyeXxKUttF1iQuVow54OrYpajdJ83pZE6chtnLteSew0UyYYMOHmprJgBpLxyQctKOalNCFms5EsyaNomviLUqzyzQSgDgYvSDvUXedQORSlVVhozJO_WIBQhaZJja4y6gNgEESvuIDYkBxvj-UqK489mR4jmxgT1s_sX7c11kZZjITyXtvJ5wP1IKO9sbqTiM9j_DDzEIdlb-0KRFnVXQOhnIXi3kg_J241__G0uu_9o95o8Epgr06fJ7JHB4mYZ94HsLMo3yaF_AXWI9Zs |
| 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=F4%3A+An+All-Purpose+Tool+for+Multivariate+Time+Series+Classification&rft.jtitle=Mathematics+%28Basel%29&rft.au=L%C3%B3pez-Oriona%2C+%C3%81ngel&rft.au=Vilar%2C+Jos%C3%A9+A.&rft.date=2021-12-01&rft.issn=2227-7390&rft.eissn=2227-7390&rft.volume=9&rft.issue=23&rft.spage=3051&rft_id=info:doi/10.3390%2Fmath9233051&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_math9233051 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-7390&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-7390&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-7390&client=summon |