{"id":"https://openalex.org/W7122758945","doi":"https://doi.org/10.1007/s41060-025-00894-w","title":"Average Volatility Dimensioning (AVD): dimension reduction technique for multivariate time series","display_name":"Average Volatility Dimensioning (AVD): dimension reduction technique for multivariate time series","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7122758945","doi":"https://doi.org/10.1007/s41060-025-00894-w"},"language":"en","primary_location":{"id":"doi:10.1007/s41060-025-00894-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00894-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00894-w.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00894-w.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122796279","display_name":"Kevin Mallinger","orcid":null},"institutions":[{"id":"https://openalex.org/I4210167190","display_name":"SBA Research","ror":"https://ror.org/05nny6x17","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210167190"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Kevin Mallinger","raw_affiliation_strings":["Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria","institution_ids":["https://openalex.org/I4210167190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106591685","display_name":"Edina Marica","orcid":null},"institutions":[{"id":"https://openalex.org/I4210167190","display_name":"SBA Research","ror":"https://ror.org/05nny6x17","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210167190"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Edina Marica","raw_affiliation_strings":["Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria","institution_ids":["https://openalex.org/I4210167190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055367812","display_name":"Sebastian Schrittwieser","orcid":"https://orcid.org/0000-0003-2115-2022"},"institutions":[{"id":"https://openalex.org/I4210105054","display_name":"Christian Doppler Laboratory for Thermoelectricity","ror":"https://ror.org/01cbw5x35","country_code":"AT","type":"facility","lineage":["https://openalex.org/I129774422","https://openalex.org/I145847075","https://openalex.org/I4210105054"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Sebastian Schrittwieser","raw_affiliation_strings":["Christian Doppler Laboratory for Assurance and Transparency in Software Protection, University of Vienna, W\u00e4hringer Stra\u00dfe 29, 1090, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Christian Doppler Laboratory for Assurance and Transparency in Software Protection, University of Vienna, W\u00e4hringer Stra\u00dfe 29, 1090, Vienna, Austria","institution_ids":["https://openalex.org/I4210105054"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057376617","display_name":"Sebastian Raubitzek","orcid":"https://orcid.org/0000-0003-2206-9263"},"institutions":[{"id":"https://openalex.org/I4210167190","display_name":"SBA Research","ror":"https://ror.org/05nny6x17","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210167190"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Sebastian Raubitzek","raw_affiliation_strings":["Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Complexity and Resilience Research Group, SBA Research gGmbH, Floragasse 7, 1040, Vienna, Austria","institution_ids":["https://openalex.org/I4210167190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055367812"],"corresponding_institution_ids":["https://openalex.org/I4210105054"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":26.7056,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.97961341,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"22","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.725600004196167,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.725600004196167,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.1404000073671341,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.03709999844431877,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensioning","display_name":"Dimensioning","score":0.7487999796867371},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.7045000195503235},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.588100016117096},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.573199987411499},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5544999837875366},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.553600013256073},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5343999862670898},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.490200012922287}],"concepts":[{"id":"https://openalex.org/C89714869","wikidata":"https://www.wikidata.org/wiki/Q2280470","display_name":"Dimensioning","level":2,"score":0.7487999796867371},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.7045000195503235},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.588100016117096},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.573199987411499},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.490200012922287},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4668999910354614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4296000003814697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.415800005197525},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4058000147342682},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.3747999966144562},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.367900013923645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3490000069141388},{"id":"https://openalex.org/C153914771","wikidata":"https://www.wikidata.org/wiki/Q5227343","display_name":"Data reduction","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.30559998750686646},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C3019022308","wikidata":"https://www.wikidata.org/wiki/Q1418353","display_name":"Multidimensional data","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2971000075340271},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C2986084806","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data series","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s41060-025-00894-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00894-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00894-w.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s41060-025-00894-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00894-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00894-w.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321691","display_name":"Universit\u00e4t Wien","ror":"https://ror.org/03prydq77"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7122758945.pdf"},"referenced_works_count":68,"referenced_works":["https://openalex.org/W1480808529","https://openalex.org/W1549386224","https://openalex.org/W1582873796","https://openalex.org/W1587559447","https://openalex.org/W1598904332","https://openalex.org/W1973192023","https://openalex.org/W1979819359","https://openalex.org/W1982506807","https://openalex.org/W1992685699","https://openalex.org/W1993855803","https://openalex.org/W1993958608","https://openalex.org/W1997575533","https://openalex.org/W1998871699","https://openalex.org/W2001141328","https://openalex.org/W2040704490","https://openalex.org/W2051848577","https://openalex.org/W2053186076","https://openalex.org/W2059489422","https://openalex.org/W2060064529","https://openalex.org/W2061402186","https://openalex.org/W2066796814","https://openalex.org/W2077979927","https://openalex.org/W2083252561","https://openalex.org/W2089468765","https://openalex.org/W2098914003","https://openalex.org/W2100967164","https://openalex.org/W2123649031","https://openalex.org/W2133487567","https://openalex.org/W2141394518","https://openalex.org/W2143834888","https://openalex.org/W2156447271","https://openalex.org/W2163922914","https://openalex.org/W2164274563","https://openalex.org/W2510030309","https://openalex.org/W2513212011","https://openalex.org/W2794775703","https://openalex.org/W2797333853","https://openalex.org/W2884459798","https://openalex.org/W2889032177","https://openalex.org/W2889326414","https://openalex.org/W2911933223","https://openalex.org/W2920795615","https://openalex.org/W2952497813","https://openalex.org/W2964110908","https://openalex.org/W2998043344","https://openalex.org/W3113308802","https://openalex.org/W3147178137","https://openalex.org/W3176621072","https://openalex.org/W3208096420","https://openalex.org/W3208826992","https://openalex.org/W4205806204","https://openalex.org/W4231009334","https://openalex.org/W4280612539","https://openalex.org/W4283702707","https://openalex.org/W4288492581","https://openalex.org/W4292081177","https://openalex.org/W4292847812","https://openalex.org/W4392160106","https://openalex.org/W4392611328","https://openalex.org/W4393554840","https://openalex.org/W4404356591","https://openalex.org/W4406666206","https://openalex.org/W6888883819","https://openalex.org/W6888925320","https://openalex.org/W6888996992","https://openalex.org/W6907946621","https://openalex.org/W6927535279","https://openalex.org/W6944998182"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Reducing":[1],"the":[2,36,42,52,69,72,78,88,116,122,130],"features":[3,39],"while":[4,135],"preserving":[5],"a":[6,11,25,46],"dataset\u2019s":[7],"inner":[8],"dynamics":[9,124],"is":[10,15,74,93],"challenging":[12],"task.":[13],"This":[14,49],"particularly":[16],"evident":[17],"for":[18,81,129],"nonlinear":[19,100],"and":[20,40,55,86,112,143],"time-dependent":[21],"processes.":[22],"We":[23],"propose":[24],"novel":[26],"dimension":[27,58],"reduction":[28,59],"technique,":[29],"Average":[30],"Volatility":[31],"Dimensioning":[32],"(AVD),":[33],"which":[34],"captures":[35],"behavior":[37],"between":[38],"reduces":[41],"multiple":[43],"dimensions":[44,80],"to":[45,67],"univariate":[47],"signal.":[48],"article":[50],"describes":[51],"developed":[53],"mechanisms":[54],"compares":[56],"this":[57],"technique":[60],"against":[61],"nine":[62],"state-of-the-art":[63],"methods.":[64],"The":[65,91],"ability":[66],"retain":[68],"information":[70],"of":[71],"data":[73,123],"evaluated":[75],"by":[76],"using":[77],"reduced":[79],"machine":[82,132],"learning":[83,133],"classification":[84],"tasks":[85],"comparing":[87],"phase-space":[89,139],"portraits.":[90],"validation":[92],"performed":[94],"on":[95],"eight":[96],"different":[97],"datasets":[98],"featuring":[99],"time":[101],"series":[102],"data,":[103],"covering":[104],"domains":[105],"such":[106],"as":[107],"movement":[108],"recognition,":[109],"fault":[110],"detection,":[111],"environmental":[113],"monitoring.":[114],"Overall,":[115],"results":[117],"show":[118],"that":[119],"AVD":[120],"encapsulates":[121],"best,":[125],"demonstrating":[126],"superior":[127],"performance":[128],"discussed":[131],"approach":[134],"maintaining":[136],"an":[137],"interpretable":[138],"trajectory,":[140],"resisting":[141],"noise,":[142],"remaining":[144],"computationally":[145],"efficient.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-27T09:02:27.158192","created_date":"2026-01-13T00:00:00"}