Time series are essential for modelling a lot of activities such as software behavior, heart beats per time, business processes. The analysis of the series data can prevent errors, boost profits, and improve the understanding of behaviors. Among the many techniques available, we can find Deep Learning techniques and Data Mining techniques. In Data Mining, distance matrices between subsequences (similarity matrices, recurrence plots) have already shown their potential on fast large-scale time series behavior analysis. In the Deep Learning, there exists different tools for analyzing the models embedding space for getting insights of the data behavior. DeepVATS is a tool for large time series analysis that allows the visual interaction within the embedding space (latent space) of Deep Learning models and the original data. The training and analysis of the model may result on a large use of computational resources, resulting in a lack of interactivity. To solve this issue, we integrate distance matrices plots within the tool. The incorporation of these plots with the associated downsampling techniques makes DeepVATS a more efficient and user-friendly tool for a first quick analysis of the data, achieving runtimes reductions of up to \\(10^4\\) seconds, allowing fast preliminary analysis of datasets of up to 7M elements. Also, this incorporation allows us to detect trends, extending its capabilities. The new functionality is tested in three use cases: the M-Toy synthetic dataset for anomaly detection, the S3 synthetic dataset for trend detection and the real-world dataset Pulsus Paradoxus for anomaly checking.
"],"publicationDate":"2024-12-04","publisher":"Springer Science and Business Media LLC","embargoEndDate":null,"sources":["Crossref"],"formats":null,"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":{"name":"Cognitive Computation","issnPrinted":"1866-9956","issnOnline":"1866-9964","issnLinking":null,"ep":null,"iss":null,"sp":null,"vol":"17","edition":null,"conferencePlace":null,"conferenceDate":null},"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_dedup___::eefca618bafe76f436c1489dc2dd5c0f","originalIds":["10394","10.1007/s12559-024-10394-x","50|doiboost____|517f5e2d651d8470c584023a7ada660e","10.21203/rs.3.rs-5196699/v1","50|doiboost____|eefca618bafe76f436c1489dc2dd5c0f","50|dblp________::9db812b0b69ada7b61c8f3a864543a5a"],"pids":[{"scheme":"doi","value":"10.1007/s12559-024-10394-x"},{"scheme":"doi","value":"10.21203/rs.3.rs-5196699/v1"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":1.0,"influence":2.3299587E-9,"popularity":2.6362512E-9,"impulse":1.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C5"}},"projects":[{"id":"corda_____he::0b1f4672a3f95803887ea3b472284aa4","code":"101168344","acronym":"TUAI","title":"Towards an Understanding of Artificial Intelligence via a transparent, open and explainable perspective","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/101168344"}]}],"organizations":[{"legalName":"Universidad Politécnica de Madrid","acronym":"UPM","id":"openorgs____::cad284878801b9465fa51a95b1d779db","pids":[{"scheme":"OrgRef","value":"1933854"},{"scheme":"wikidata","value":"Q5838945"},{"scheme":"ROR","value":"https://ror.org/03n6nwv02"},{"scheme":"fundref","value":"501100003759"},{"scheme":"wikidata","value":"Q25864"},{"scheme":"fundref","value":"501100023545"},{"scheme":"mag_id","value":"88060688"},{"scheme":"GRID","value":"grid.5690.a"},{"scheme":"PIC","value":"999974844"},{"scheme":"FundRef","value":"501100003759"},{"scheme":"ISNI","value":"0000000121512978"},{"scheme":"OrgReg","value":"ES0041"},{"scheme":"Wikidata","value":"Q5838945"},{"scheme":"FundRef","value":"501100023545"},{"scheme":"Wikidata","value":"Q25864"}],"countries":[{"code":"ES","label":"Spain"}],"websiteurl":"http://www.upm.es"}],"communities":null,"collectedFrom":[{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"},{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"}],"instances":[{"pids":[{"scheme":"doi","value":"10.1007/s12559-024-10394-x"}],"license":"Springer Nature TDM","accessRight":{"code":"c_14cb","label":"CLOSED","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":null},"type":"Article","urls":["https://doi.org/10.1007/s12559-024-10394-x"],"publicationDate":"2024-12-16","refereed":"peerReviewed","hostedBy":{"key":"issn___print::95ddd13b46ff976d709b9fec02fa4479","value":"Cognitive Computation"},"collectedFrom":{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"}},{"pids":[{"scheme":"doi","value":"10.21203/rs.3.rs-5196699/v1"}],"license":"CC BY","accessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":"hybrid"},"type":"Article","urls":["https://doi.org/10.21203/rs.3.rs-5196699/v1"],"publicationDate":"2024-12-04","refereed":"peerReviewed","hostedBy":{"key":"openaire____::55045bd2a65019fd8e6741a755395c8c","value":"Unknown Repository"},"collectedFrom":{"key":"openaire____::081b82f96300b6a6e3d282bad31cb6e2","value":"Crossref"}},{"alternateIdentifiers":[{"scheme":"doi","value":"10.1007/s12559-024-10394-x"}],"type":"Article","urls":["https://dblp.org/rec/journals/cogcom/SantamariaValenzuelaRC25.html","https://doi.org/10.1007/s12559-024-10394-x"],"publicationDate":"2025-01-01","refereed":"nonPeerReviewed","hostedBy":{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"},"collectedFrom":{"key":"openaire____::d8b68cc0a53121f6f896883a7d60c1db","value":"DBLP"}}],"links":[{"header":{"relationType":"resultProject","relationClass":"isProducedBy","relatedIdentifier":"corda_____he::0b1f4672a3f95803887ea3b472284aa4","relatedRecordType":"project","relationProvenance":"iis","trust":"0.897"},"collectedfrom":[{"dsId":"openaire____::3f264f93cf3b0cfc4ede188a6300455c","dsName":"CORDA - COmmon Research DAta Warehouse - Horizon Europe"}],"projectTitle":"Towards an Understanding of Artificial Intelligence via a transparent, open and explainable perspective","code":"101168344","funding":{"funder":{"id":"ec__________::EC","shortname":"EC","name":"European Commission","jurisdiction":{"code":"EU","label":"European Union"},"pid":null},"level0":{"id":"ec__________::EC::HE","description":"Horizon Europe Framework Programme","name":"HE"},"level1":{"id":"ec__________::EC::HE::HORIZON-TMA-MSCA-DN","description":"HORIZON TMA MSCA Doctoral Networks","name":"HORIZON-TMA-MSCA-DN"},"level2":{"id":null,"description":null,"name":null}},"startDate":"2024-10-01","endDate":"2028-09-30"}],"otherTitles":null,"green":false,"inDiamondJournal":false,"isGreen":false,"isInDiamondJournal":false}