Clustering and classification of time series using topological data analysis with applications to finance
In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA) such as persistent homology and time delay embedding for analysing time-series data. We present a new clustering method SOM-TDA and a new clas...
Saved in:
| Published in: | Expert systems with applications Vol. 162; p. 113868 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Ltd
30.12.2020
Elsevier BV |
| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA) such as persistent homology and time delay embedding for analysing time-series data. We present a new clustering method SOM-TDA and a new classification method RF-TDA based on TDA. Using SOM-TDA we examine the topological similarities and dissimilarities of some well-known time-series models used in finance. We also use the RF-TDA to examine if the topological features can be used to distinguish between time series models using simulated data. The performance of RF-TDA on the classification task is compared against three other classification methods. We also consider an application of RF-TDA to financial time series classification using real-life price data of stocks belonging to different sectors. RF-TDA is seen to perform quite well in the two experiments based on real-life stock-price data. This implies that the topological features of the time series of stock prices in the different sectors are not identical and have distinctive features that can be discerned through the use of TDA. We also briefly consider multi-class classification using RF-TDA.
•New methods for time series clustering (SOM-TDA) and classification (RF-TDA).•RF-TDA outperforms other methods on the classification task.•Dependence of stock price movements on sectors in NSE is revealed using RF-TDA. |
|---|---|
| AbstractList | In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA) such as persistent homology and time delay embedding for analysing time-series data. We present a new clustering method SOM-TDA and a new classification method RF-TDA based on TDA. Using SOM-TDA we examine the topological similarities and dissimilarities of some well-known time-series models used in finance. We also use the RF-TDA to examine if the topological features can be used to distinguish between time series models using simulated data. The performance of RF-TDA on the classification task is compared against three other classification methods. We also consider an application of RF-TDA to financial time series classification using real-life price data of stocks belonging to different sectors. RF-TDA is seen to perform quite well in the two experiments based on real-life stock-price data. This implies that the topological features of the time series of stock prices in the different sectors are not identical and have distinctive features that can be discerned through the use of TDA. We also briefly consider multi-class classification using RF-TDA.
•New methods for time series clustering (SOM-TDA) and classification (RF-TDA).•RF-TDA outperforms other methods on the classification task.•Dependence of stock price movements on sectors in NSE is revealed using RF-TDA. In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA) such as persistent homology and time delay embedding for analysing time-series data. We present a new clustering method SOM-TDA and a new classification method RF-TDA based on TDA. Using SOM-TDA we examine the topological similarities and dissimilarities of some well-known time-series models used in finance. We also use the RF-TDA to examine if the topological features can be used to distinguish between time series models using simulated data. The performance of RF-TDA on the classification task is compared against three other classification methods. We also consider an application of RF-TDA to financial time series classification using real-life price data of stocks belonging to different sectors. RF-TDA is seen to perform quite well in the two experiments based on real-life stock-price data. This implies that the topological features of the time series of stock prices in the different sectors are not identical and have distinctive features that can be discerned through the use of TDA. We also briefly consider multi-class classification using RF-TDA. |
| ArticleNumber | 113868 |
| Author | Majumdar, Sourav Laha, Arnab Kumar |
| Author_xml | – sequence: 1 givenname: Sourav surname: Majumdar fullname: Majumdar, Sourav email: phd18souravm@iima.ac.in – sequence: 2 givenname: Arnab Kumar surname: Laha fullname: Laha, Arnab Kumar email: arnab@iima.ac.in |
| BookMark | eNp9kMtKxDAUQIMoOI7-gKuA6455tEkG3MjgCwbc6DqkaaIZalNzM4p_b2pduXAVuJxzyT0n6HCIg0PonJIVJVRc7lYOPs2KEVYGlCuhDtCCKskrIdf8EC3IupFVTWV9jE4AdoRQSYhcoLDp95BdCsMLNkOHbW8Agg_W5BAHHD3O4c1hKIQDvIeJy3GMfXwpTI87k00RTf8FAfBnyK_YjGP_60NhsQ-DGaw7RUfe9ODOft8ler69edrcV9vHu4fN9baynKlcSdFJ1SguOPG0jCzntmbGcCaNZ5T7Zu2VE75pWNMa6RlXvBWdXcuWU9IqvkQX894xxfe9g6x3cZ_KD0GzWjS1FErRQrGZsikCJOf1mMKbSV-aEj0l1Ts9JdVTUj0nLZL6I9mQfw7NyYT-f_VqVl05_SO4pMEGV7J0ITmbdRfDf_o34kCVLQ |
| CitedBy_id | crossref_primary_10_1007_s41066_024_00488_0 crossref_primary_10_1016_j_engappai_2023_107716 crossref_primary_10_3389_fams_2022_940133 crossref_primary_10_1109_ACCESS_2024_3481652 crossref_primary_10_1007_s11634_023_00578_y crossref_primary_10_1007_s11042_022_12133_6 crossref_primary_10_1016_j_bspc_2022_104396 crossref_primary_10_3390_ijgi14060226 crossref_primary_10_1016_j_eswa_2021_115326 crossref_primary_10_1007_s11227_021_04303_4 crossref_primary_10_1016_j_fub_2025_100059 crossref_primary_10_1016_j_chaos_2025_116054 crossref_primary_10_1007_s00500_022_07630_7 crossref_primary_10_1155_2022_5985733 crossref_primary_10_1111_tgis_13149 crossref_primary_10_1016_j_jneumeth_2021_109324 crossref_primary_10_1016_j_eswa_2022_116629 crossref_primary_10_1016_j_eswa_2025_126884 crossref_primary_10_3390_math13020325 crossref_primary_10_20965_jaciii_2025_p0721 crossref_primary_10_3390_sym16091236 crossref_primary_10_1007_s10661_024_13477_2 crossref_primary_10_1016_j_neucom_2023_127173 crossref_primary_10_1016_j_dajour_2023_100253 crossref_primary_10_3390_math9091046 crossref_primary_10_1016_j_physa_2024_129785 crossref_primary_10_1002_cpe_7732 crossref_primary_10_1016_j_knosys_2024_112768 crossref_primary_10_1007_s11634_023_00548_4 crossref_primary_10_1016_j_knosys_2021_107153 crossref_primary_10_1016_j_eswa_2023_120632 crossref_primary_10_1080_14697688_2025_2544762 crossref_primary_10_21511_imfi_19_4__2022_22 crossref_primary_10_1007_s10489_021_02788_3 crossref_primary_10_1057_s41283_022_00110_0 crossref_primary_10_1016_j_artmed_2023_102492 crossref_primary_10_1155_2022_7400833 |
| Cites_doi | 10.1090/noti1869 10.1186/s12859-015-0645-6 10.1007/s00454-006-1276-5 10.1007/s00454-002-2885-2 10.1007/s10208-014-9206-z 10.1007/s10618-016-0483-9 10.1023/A:1010933404324 10.1214/aoms/1177707045 10.1016/j.csda.2018.05.019 10.1016/j.eswa.2020.113222 10.1016/j.csda.2007.05.010 10.1146/annurev-financial-110311-101808 10.3934/mbe.2014.11.723 10.1016/0167-2789(86)90031-X 10.1016/0304-405X(93)90023-5 10.1016/j.physa.2017.09.028 10.1016/j.eswa.2015.04.010 10.1016/S0167-2789(97)00118-8 10.1088/0266-5611/27/12/124007 10.1007/BF00337288 10.1007/s00454-004-1146-y 10.1103/PhysRevA.45.3403 10.1038/srep01236 10.1111/j.1467-9892.1980.tb00297.x |
| ContentType | Journal Article |
| Copyright | 2020 Elsevier Ltd Copyright Elsevier BV Dec 30, 2020 |
| Copyright_xml | – notice: 2020 Elsevier Ltd – notice: Copyright Elsevier BV Dec 30, 2020 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2020.113868 |
| 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 |
| ExternalDocumentID | 10_1016_j_eswa_2020_113868 S095741742030676X |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AATTM AAXKI AAXUO AAYFN ABBOA ABFNM ABJNI ABMAC ABMVD ABUCO ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEIPS AEKER AENEX AFTJW AGHFR AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AKRWK ALEQD ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNPGV BNSAS CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSH SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AAYWO AAYXX ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ XPP ZMT ~HD 7SC 8FD AFXIZ AGCQF AGRNS JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c328t-76d78583630f1c32c33c42aa327af213f59f8e6f5525ba7f2383b6dc97b310b83 |
| ISICitedReferencesCount | 58 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000582113700043&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 07:49:24 EDT 2025 Sat Nov 29 07:07:24 EST 2025 Tue Nov 18 21:40:59 EST 2025 Sun Apr 06 06:53:31 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Random forest Persistent homology Self organizing maps Time delay embedding Takens theorem |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c328t-76d78583630f1c32c33c42aa327af213f59f8e6f5525ba7f2383b6dc97b310b83 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2465476881 |
| PQPubID | 2045477 |
| ParticipantIDs | proquest_journals_2465476881 crossref_primary_10_1016_j_eswa_2020_113868 crossref_citationtrail_10_1016_j_eswa_2020_113868 elsevier_sciencedirect_doi_10_1016_j_eswa_2020_113868 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-12-30 |
| PublicationDateYYYYMMDD | 2020-12-30 |
| PublicationDate_xml | – month: 12 year: 2020 text: 2020-12-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Edelsbrunner, Harer (b13) 2010 Yin (b42) 2008 Goel, Pasricha, Mehra (b19) 2020 (b28) 2020 Zomorodian, Carlsson (b44) 2005; 33 Edelsbrunner, Letscher, Zomorodian (b14) 2002; 28 Maharaj, Alonso (b27) 2007; 52 Goodson (b20) 2016 Kohonen (b25) 1982; 43 Cohen-Steiner, Edelsbrunner, Harer (b10) 2007; 37 Gidea, Katz (b18) 2018; 491 Perea, Harer (b34) 2015; 15 Bubenik (b8) 2015; 16 Veenstra (b41) 2012 Cao (b9) 1997; 110 Kim, Kim, Rinaldo (b24) 2018 Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Gidea, Goldsmith, Katz, Roldan, Shmalo (b17) 2018 Hyndman, Athanasopoulos, Bergmeir, Caceres, Chhay, O’Hara-Wild, Petropoulos, Razbash, Wang, Yasmeen (b22) 2020 Munkres (b30) 2018 Fasy, Kim, Lecci, Maria (b16) 2014 Kennel, Brown, Abarbanel (b23) 1992; 45 Shumway, Stoffer (b36) 2017 Ang, Timmermann (b1) 2012; 4 Granger, Joyeux (b21) 1980; 1 Trapletti, Hornik (b38) 2019 Fama, French (b15) 1993; 33 Batal, Hauskrecht (b3) 2009 Broomhead, King (b7) 1986; 20 Perea, Deckard, Haase, Harer (b33) 2015; 16 Perea (b32) 2019; 66 Berwald, Gidea (b5) 2014; 11 Umeda (b40) 2017; 12 Seattle, WA. Pereira, de Mello (b35) 2015; 42 Breiman (b6) 2001; 45 Mileyko, Mukherjee, Harer (b29) 2011; 27 Percival, Walden (b31) 2000 Truong (b39) 2017 Bagnall, Lines, Bostrom, Large, Keogh (b2) 2017; 31 Zhao, Barber, Taylor, Milan (b43) 2018; 127 de Silva, Skraba, Vejdemo-Johansson (b11) 2012 Dwass (b12) 1957 Lum, Singh, Lehman, Ishkanov, Vejdemo-Johansson, Alagappan, Carlsson, Carlsson (b26) 2013; 3 Takens (b37) 1981 Broomhead (10.1016/j.eswa.2020.113868_b7) 1986; 20 Fasy (10.1016/j.eswa.2020.113868_b16) 2014 Perea (10.1016/j.eswa.2020.113868_b34) 2015; 15 Berwald (10.1016/j.eswa.2020.113868_b5) 2014; 11 (10.1016/j.eswa.2020.113868_b28) 2020 Edelsbrunner (10.1016/j.eswa.2020.113868_b14) 2002; 28 Mileyko (10.1016/j.eswa.2020.113868_b29) 2011; 27 Munkres (10.1016/j.eswa.2020.113868_b30) 2018 Veenstra (10.1016/j.eswa.2020.113868_b41) 2012 Hyndman (10.1016/j.eswa.2020.113868_b22) 2020 Kohonen (10.1016/j.eswa.2020.113868_b25) 1982; 43 Lum (10.1016/j.eswa.2020.113868_b26) 2013; 3 Cao (10.1016/j.eswa.2020.113868_b9) 1997; 110 Batal (10.1016/j.eswa.2020.113868_b3) 2009 10.1016/j.eswa.2020.113868_b4 Gidea (10.1016/j.eswa.2020.113868_b17) 2018 Gidea (10.1016/j.eswa.2020.113868_b18) 2018; 491 Perea (10.1016/j.eswa.2020.113868_b32) 2019; 66 Trapletti (10.1016/j.eswa.2020.113868_b38) 2019 Kim (10.1016/j.eswa.2020.113868_b24) 2018 Bubenik (10.1016/j.eswa.2020.113868_b8) 2015; 16 Cohen-Steiner (10.1016/j.eswa.2020.113868_b10) 2007; 37 Fama (10.1016/j.eswa.2020.113868_b15) 1993; 33 Kennel (10.1016/j.eswa.2020.113868_b23) 1992; 45 Goodson (10.1016/j.eswa.2020.113868_b20) 2016 Truong (10.1016/j.eswa.2020.113868_b39) 2017 Perea (10.1016/j.eswa.2020.113868_b33) 2015; 16 de Silva (10.1016/j.eswa.2020.113868_b11) 2012 Edelsbrunner (10.1016/j.eswa.2020.113868_b13) 2010 Bagnall (10.1016/j.eswa.2020.113868_b2) 2017; 31 Granger (10.1016/j.eswa.2020.113868_b21) 1980; 1 Pereira (10.1016/j.eswa.2020.113868_b35) 2015; 42 Takens (10.1016/j.eswa.2020.113868_b37) 1981 Zhao (10.1016/j.eswa.2020.113868_b43) 2018; 127 Goel (10.1016/j.eswa.2020.113868_b19) 2020 Dwass (10.1016/j.eswa.2020.113868_b12) 1957 Breiman (10.1016/j.eswa.2020.113868_b6) 2001; 45 Yin (10.1016/j.eswa.2020.113868_b42) 2008 Ang (10.1016/j.eswa.2020.113868_b1) 2012; 4 Maharaj (10.1016/j.eswa.2020.113868_b27) 2007; 52 Umeda (10.1016/j.eswa.2020.113868_b40) 2017; 12 Shumway (10.1016/j.eswa.2020.113868_b36) 2017 Percival (10.1016/j.eswa.2020.113868_b31) 2000 Zomorodian (10.1016/j.eswa.2020.113868_b44) 2005; 33 |
| References_xml | – volume: 12 start-page: 228 year: 2017 end-page: 239 ident: b40 article-title: Time series classification via topological data analysis publication-title: Information and Media Technologies – start-page: 181 year: 1957 end-page: 187 ident: b12 article-title: Modified randomization tests for nonparametric hypotheses publication-title: The Annals of Mathematical Statistics – reference: . Seattle, WA. – volume: 66 year: 2019 ident: b32 article-title: Topological time series analysis publication-title: Notices of the American Mathematical Society – volume: 16 start-page: 77 year: 2015 end-page: 102 ident: b8 article-title: Statistical topological data analysis using persistence landscapes publication-title: Journal of Machine Learning Research (JMLR) – volume: 110 start-page: 43 year: 1997 end-page: 50 ident: b9 article-title: Practical method for determining the minimum embedding dimension of a scalar time series publication-title: Physica D: Nonlinear Phenomena – volume: 42 start-page: 6026 year: 2015 end-page: 6038 ident: b35 article-title: Persistent homology for time series and spatial data clustering publication-title: Expert Systems with Applications – reference: Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In – year: 2018 ident: b30 article-title: Elements of algebraic topology – year: 2018 ident: b17 article-title: Topological recognition of critical transitions in time series of cryptocurrencies – volume: 27 year: 2011 ident: b29 article-title: Probability measures on the space of persistence diagrams publication-title: Inverse Problems – year: 2012 ident: b11 article-title: Topological analysis of recurrent systems publication-title: Workshop on algebraic topology and machine learning, NIPS – volume: 11 start-page: 723 year: 2014 end-page: 740 ident: b5 article-title: Critical transitions in a model of a genetic regulatory system publication-title: Mathematical Biosciences & Engineering – volume: 45 start-page: 3403 year: 1992 ident: b23 article-title: Determining embedding dimension for phase-space reconstruction using a geometrical construction publication-title: Physical Review A – volume: 4 start-page: 313 year: 2012 end-page: 337 ident: b1 article-title: Regime changes and financial markets publication-title: Annual Review of Financial Economics – year: 2010 ident: b13 article-title: Computational topology: an introduction – volume: 127 start-page: 204 year: 2018 end-page: 216 ident: b43 article-title: Classification tree methods for panel data using wavelet-transformed time series publication-title: Computational Statistics & Data Analysis – volume: 20 start-page: 217 year: 1986 end-page: 236 ident: b7 article-title: Extracting qualitative dynamics from experimental data publication-title: Physica D: Nonlinear Phenomena – year: 2016 ident: b20 article-title: Chaotic dynamics: fractals, tilings, and substitutions – volume: 16 start-page: 257 year: 2015 ident: b33 article-title: SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data publication-title: BMC Bioinformatics – volume: 1 start-page: 15 year: 1980 end-page: 29 ident: b21 article-title: An introduction to long-memory time series models and fractional differencing publication-title: Journal of Time Series Analysis – volume: 491 start-page: 820 year: 2018 end-page: 834 ident: b18 article-title: Topological data analysis of financial time series: Landscapes of crashes publication-title: Physica A. Statistical Mechanics and its Applications – year: 2020 ident: b19 article-title: Topological data analysis in investment decisions publication-title: Expert Systems with Applications – start-page: 715 year: 2008 end-page: 762 ident: b42 article-title: The self-organizing maps: background, theories, extensions and applications publication-title: Computational intelligence: A compendium – year: 2017 ident: b39 article-title: An exploration of topological properties of high-frequency one-dimensional financial time series data using TDA – year: 2020 ident: b28 article-title: Methodology document of NIFTY sectoral index series – volume: 15 start-page: 799 year: 2015 end-page: 838 ident: b34 article-title: Sliding windows and persistence: An application of topological methods to signal analysis publication-title: Foundations of Computational Mathematics – start-page: 366 year: 1981 end-page: 381 ident: b37 article-title: Detecting strange attractors in turbulence publication-title: Dynamical systems and turbulence, Warwick 1980 – year: 2017 ident: b36 article-title: Time series analysis and its applications: With R examples – volume: 31 start-page: 606 year: 2017 end-page: 660 ident: b2 article-title: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances publication-title: Data Mining and Knowledge Discovery – volume: 52 start-page: 879 year: 2007 end-page: 895 ident: b27 article-title: Discrimination of locally stationary time series using wavelets publication-title: Computational Statistics & Data Analysis – volume: 33 start-page: 249 year: 2005 end-page: 274 ident: b44 article-title: Computing persistent homology publication-title: Discrete & Computational Geometry – year: 2012 ident: b41 article-title: Persistence and anti-persistence: Theory and software – volume: 43 start-page: 59 year: 1982 end-page: 69 ident: b25 article-title: Self-organized formation of topologically correct feature maps publication-title: Biological Cybernetics – volume: 37 start-page: 103 year: 2007 end-page: 120 ident: b10 article-title: Stability of persistence diagrams publication-title: Discrete & Computational Geometry – volume: 28 start-page: 511 year: 2002 end-page: 533 ident: b14 article-title: Topological persistence and simplification publication-title: Discrete & Computational Geometry – volume: 33 start-page: 3 year: 1993 end-page: 56 ident: b15 article-title: Common risk factors in the returns on stocks and bonds publication-title: Journal of Financial Economic – volume: 3 start-page: 1236 year: 2013 ident: b26 article-title: Extracting insights from the shape of complex data using topology publication-title: Scientific Reports – year: 2000 ident: b31 article-title: Wavelet methods for time series analysis, Vol. 4 – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b6 article-title: Random forests publication-title: Machine Learning – year: 2019 ident: b38 article-title: Tseries: Time series analysis and computational finance – year: 2020 ident: b22 article-title: Forecast: Forecasting functions for time series and linear models – year: 2018 ident: b24 article-title: Time series featurization via topological data analysis: an application to cryptocurrency trend forecasting – year: 2014 ident: b16 article-title: Introduction to the R package TDA – start-page: 735 year: 2009 end-page: 739 ident: b3 article-title: A supervised time series feature extraction technique using dct and dwt publication-title: 2009 international conference on machine learning and applications – volume: 66 issue: 5 year: 2019 ident: 10.1016/j.eswa.2020.113868_b32 article-title: Topological time series analysis publication-title: Notices of the American Mathematical Society doi: 10.1090/noti1869 – year: 2017 ident: 10.1016/j.eswa.2020.113868_b36 – volume: 16 start-page: 77 issue: 1 year: 2015 ident: 10.1016/j.eswa.2020.113868_b8 article-title: Statistical topological data analysis using persistence landscapes publication-title: Journal of Machine Learning Research (JMLR) – year: 2020 ident: 10.1016/j.eswa.2020.113868_b22 – year: 2000 ident: 10.1016/j.eswa.2020.113868_b31 – year: 2012 ident: 10.1016/j.eswa.2020.113868_b11 article-title: Topological analysis of recurrent systems – year: 2014 ident: 10.1016/j.eswa.2020.113868_b16 – volume: 16 start-page: 257 issue: 1 year: 2015 ident: 10.1016/j.eswa.2020.113868_b33 article-title: SW1PerS: Sliding windows and 1-persistence scoring; discovering periodicity in gene expression time series data publication-title: BMC Bioinformatics doi: 10.1186/s12859-015-0645-6 – volume: 37 start-page: 103 issue: 1 year: 2007 ident: 10.1016/j.eswa.2020.113868_b10 article-title: Stability of persistence diagrams publication-title: Discrete & Computational Geometry doi: 10.1007/s00454-006-1276-5 – volume: 28 start-page: 511 year: 2002 ident: 10.1016/j.eswa.2020.113868_b14 article-title: Topological persistence and simplification publication-title: Discrete & Computational Geometry doi: 10.1007/s00454-002-2885-2 – volume: 15 start-page: 799 issue: 3 year: 2015 ident: 10.1016/j.eswa.2020.113868_b34 article-title: Sliding windows and persistence: An application of topological methods to signal analysis publication-title: Foundations of Computational Mathematics doi: 10.1007/s10208-014-9206-z – volume: 31 start-page: 606 issue: 3 year: 2017 ident: 10.1016/j.eswa.2020.113868_b2 article-title: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances publication-title: Data Mining and Knowledge Discovery doi: 10.1007/s10618-016-0483-9 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.eswa.2020.113868_b6 article-title: Random forests publication-title: Machine Learning doi: 10.1023/A:1010933404324 – start-page: 181 year: 1957 ident: 10.1016/j.eswa.2020.113868_b12 article-title: Modified randomization tests for nonparametric hypotheses publication-title: The Annals of Mathematical Statistics doi: 10.1214/aoms/1177707045 – volume: 127 start-page: 204 year: 2018 ident: 10.1016/j.eswa.2020.113868_b43 article-title: Classification tree methods for panel data using wavelet-transformed time series publication-title: Computational Statistics & Data Analysis doi: 10.1016/j.csda.2018.05.019 – year: 2018 ident: 10.1016/j.eswa.2020.113868_b17 – year: 2016 ident: 10.1016/j.eswa.2020.113868_b20 – year: 2020 ident: 10.1016/j.eswa.2020.113868_b19 article-title: Topological data analysis in investment decisions publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113222 – year: 2018 ident: 10.1016/j.eswa.2020.113868_b30 – ident: 10.1016/j.eswa.2020.113868_b4 – year: 2012 ident: 10.1016/j.eswa.2020.113868_b41 – volume: 52 start-page: 879 issue: 2 year: 2007 ident: 10.1016/j.eswa.2020.113868_b27 article-title: Discrimination of locally stationary time series using wavelets publication-title: Computational Statistics & Data Analysis doi: 10.1016/j.csda.2007.05.010 – year: 2017 ident: 10.1016/j.eswa.2020.113868_b39 – volume: 4 start-page: 313 issue: 1 year: 2012 ident: 10.1016/j.eswa.2020.113868_b1 article-title: Regime changes and financial markets publication-title: Annual Review of Financial Economics doi: 10.1146/annurev-financial-110311-101808 – start-page: 735 year: 2009 ident: 10.1016/j.eswa.2020.113868_b3 article-title: A supervised time series feature extraction technique using dct and dwt – volume: 11 start-page: 723 issue: 4 year: 2014 ident: 10.1016/j.eswa.2020.113868_b5 article-title: Critical transitions in a model of a genetic regulatory system publication-title: Mathematical Biosciences & Engineering doi: 10.3934/mbe.2014.11.723 – volume: 20 start-page: 217 issue: 2–3 year: 1986 ident: 10.1016/j.eswa.2020.113868_b7 article-title: Extracting qualitative dynamics from experimental data publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/0167-2789(86)90031-X – volume: 33 start-page: 3 year: 1993 ident: 10.1016/j.eswa.2020.113868_b15 article-title: Common risk factors in the returns on stocks and bonds publication-title: Journal of Financial Economic doi: 10.1016/0304-405X(93)90023-5 – volume: 491 start-page: 820 year: 2018 ident: 10.1016/j.eswa.2020.113868_b18 article-title: Topological data analysis of financial time series: Landscapes of crashes publication-title: Physica A. Statistical Mechanics and its Applications doi: 10.1016/j.physa.2017.09.028 – year: 2010 ident: 10.1016/j.eswa.2020.113868_b13 – volume: 42 start-page: 6026 issue: 15–16 year: 2015 ident: 10.1016/j.eswa.2020.113868_b35 article-title: Persistent homology for time series and spatial data clustering publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.04.010 – volume: 110 start-page: 43 issue: 1–2 year: 1997 ident: 10.1016/j.eswa.2020.113868_b9 article-title: Practical method for determining the minimum embedding dimension of a scalar time series publication-title: Physica D: Nonlinear Phenomena doi: 10.1016/S0167-2789(97)00118-8 – volume: 12 start-page: 228 year: 2017 ident: 10.1016/j.eswa.2020.113868_b40 article-title: Time series classification via topological data analysis publication-title: Information and Media Technologies – year: 2020 ident: 10.1016/j.eswa.2020.113868_b28 – start-page: 715 year: 2008 ident: 10.1016/j.eswa.2020.113868_b42 article-title: The self-organizing maps: background, theories, extensions and applications – year: 2018 ident: 10.1016/j.eswa.2020.113868_b24 – volume: 27 issue: 12 year: 2011 ident: 10.1016/j.eswa.2020.113868_b29 article-title: Probability measures on the space of persistence diagrams publication-title: Inverse Problems doi: 10.1088/0266-5611/27/12/124007 – volume: 43 start-page: 59 issue: 1 year: 1982 ident: 10.1016/j.eswa.2020.113868_b25 article-title: Self-organized formation of topologically correct feature maps publication-title: Biological Cybernetics doi: 10.1007/BF00337288 – volume: 33 start-page: 249 issue: 2 year: 2005 ident: 10.1016/j.eswa.2020.113868_b44 article-title: Computing persistent homology publication-title: Discrete & Computational Geometry doi: 10.1007/s00454-004-1146-y – volume: 45 start-page: 3403 issue: 6 year: 1992 ident: 10.1016/j.eswa.2020.113868_b23 article-title: Determining embedding dimension for phase-space reconstruction using a geometrical construction publication-title: Physical Review A doi: 10.1103/PhysRevA.45.3403 – volume: 3 start-page: 1236 year: 2013 ident: 10.1016/j.eswa.2020.113868_b26 article-title: Extracting insights from the shape of complex data using topology publication-title: Scientific Reports doi: 10.1038/srep01236 – start-page: 366 year: 1981 ident: 10.1016/j.eswa.2020.113868_b37 article-title: Detecting strange attractors in turbulence – year: 2019 ident: 10.1016/j.eswa.2020.113868_b38 – volume: 1 start-page: 15 issue: 1 year: 1980 ident: 10.1016/j.eswa.2020.113868_b21 article-title: An introduction to long-memory time series models and fractional differencing publication-title: Journal of Time Series Analysis doi: 10.1111/j.1467-9892.1980.tb00297.x |
| SSID | ssj0017007 |
| Score | 2.5974321 |
| Snippet | In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA)... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 113868 |
| SubjectTerms | Classification Clustering Data analysis Finance Homology Persistent homology Random forest Self organizing maps Takens theorem Time delay embedding Time lag Time series Topology |
| Title | Clustering and classification of time series using topological data analysis with applications to finance |
| URI | https://dx.doi.org/10.1016/j.eswa.2020.113868 https://www.proquest.com/docview/2465476881 |
| Volume | 162 |
| WOSCitedRecordID | wos000582113700043&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: ScienceDirect Freedom Collection 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/eLvHCXMwtV3Nb9MwFLfKxoEL34jBQD4gLlWmxq5j51hNnQBNBYkO9WbZji2t6rqypmV_Ps8fSdtpVHDgElVO3CZ-v7w-v68fQh-oZn3ulM7Kquz5DUqR6Tw3mXaMaK7AALcqkE3w0UhMJuW3Tmfd1MKsZ3w-F7e35eK_ihrGQNi-dPYfxN1-KQzAZxA6HEHscPwrwZ_OVr75QVN8aLx57POBWtvQ08l3_a3YZXcVXAV1ZEqIERtV-zKt1Koklr5tBbm9repCl46dHKLQMblOfaHvmbZxfE9XV1VM6v4OT6PWbUaQiqGnATyh7obM722PBAnZHSm4EtxkbanMjx13I8_6eWTkObFR2QpOs4JHhsRWGyflHPVpfq-Wjw6H6Yld_vKto0hgphGRnme3pfboqzy7OD-X4-Fk_HHxM_NsYz4qn6hXHqBDwlkJ2vBw8Hk4-dLGn3gvFto3d53KrWJm4N2f_ZNJc-fPPVgs46focdpq4EGEyDPUsfPn6ElD44GTVn-BLjeIwYAYvIsYfO2wRwyOiMEBMXgLMdgjBjeIwV70eFv0cC1OiHmJLs6G49NPWWLgyAwlos54UXHBBC1oz-UwZCg1faIUJVw5klPHSids4RgjTCvuwP6juqhMyTVsG7Sgr9DB_HpuXyMM56rCWFoox_rWaVWVVFWw2y2FchU1RyhvFlGa1J7es6TMZJOHOJV-4aVfeBkX_gh12zmL2Jxl79WskY1M5mU0GyXgau-840aQMr3nS0l8H0LYqov8zf7Tb9GjzTtyjA7qm5V9hx6adX25vHmfcPcb6YWo9w |
| 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=Clustering+and+classification+of+time+series+using+topological+data+analysis+with+applications+to+finance&rft.jtitle=Expert+systems+with+applications&rft.au=Majumdar%2C+Sourav&rft.au=Laha%2C+Arnab+Kumar&rft.date=2020-12-30&rft.pub=Elsevier+BV&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=162&rft.spage=1&rft_id=info:doi/10.1016%2Fj.eswa.2020.113868&rft.externalDBID=NO_FULL_TEXT |
| 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 |