{"id":"https://openalex.org/W3107975486","doi":"https://doi.org/10.1145/3447548.3467166","title":"TimeSHAP: Explaining Recurrent Models through Sequence Perturbations","display_name":"TimeSHAP: Explaining Recurrent Models through Sequence Perturbations","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3107975486","doi":"https://doi.org/10.1145/3447548.3467166","mag":"3107975486"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467166","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467166","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467166","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467166","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jo\u00e3o Bento","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jo\u00e3o Bento","raw_affiliation_strings":["Feedzai, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feedzai, Lisboa, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pedro Saleiro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pedro Saleiro","raw_affiliation_strings":["Feedzai, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feedzai, Lisboa, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Andr\u00e9 F. Cruz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andr\u00e9 F. Cruz","raw_affiliation_strings":["Feedzai, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feedzai, Lisboa, Portugal","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"M\u00e1rio A.T. Figueiredo","orcid":null},"institutions":[{"id":"https://openalex.org/I141596103","display_name":"University of Lisbon","ror":"https://ror.org/01c27hj86","country_code":"PT","type":"education","lineage":["https://openalex.org/I141596103"]},{"id":"https://openalex.org/I203847022","display_name":"Instituto Polit\u00e9cnico de Lisboa","ror":"https://ror.org/04ea70f07","country_code":"PT","type":"education","lineage":["https://openalex.org/I203847022"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"M\u00e1rio A.T. Figueiredo","raw_affiliation_strings":["Instituto Superior T\u00e9cnico, University of Lisbon, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto Superior T\u00e9cnico, University of Lisbon, Lisboa, Portugal","institution_ids":["https://openalex.org/I203847022","https://openalex.org/I141596103"]}]},{"author_position":"last","author":{"id":null,"display_name":"Pedro Bizarro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pedro Bizarro","raw_affiliation_strings":["Feedzai, Lisboa, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feedzai, Lisboa, Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.6833,"has_fulltext":true,"cited_by_count":94,"citation_normalized_percentile":{"value":0.96932174,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2565","last_page":"2573"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991000294685364,"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":0.9991000294685364,"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/T10028","display_name":"Topic Modeling","score":0.9965000152587891,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9962999820709229,"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/pruning","display_name":"Pruning","score":0.6269000172615051},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6093999743461609},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.604200005531311},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5706999897956848},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.5534999966621399},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5393000245094299},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5144000053405762}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6269000172615051},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.604200005531311},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5706999897956848},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.5534999966621399},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5393000245094299},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.503600001335144},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46459999680519104},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3928000032901764},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3109999895095825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30649998784065247},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.30169999599456787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2782999873161316},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3447548.3467166","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467166","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467166","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2012.00073","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2012.00073","pdf_url":"https://arxiv.org/pdf/2012.00073","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3447548.3467166","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3447548.3467166","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3447548.3467166","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1167214205","display_name":null,"funder_award_id":"COMPETE","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G1578237752","display_name":null,"funder_award_id":"COMPETE","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G2144570049","display_name":null,"funder_award_id":"UIDB/50008","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G274581676","display_name":null,"funder_award_id":"POCI-01","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G3375535518","display_name":"Instituto de Telecomunica\u00e7\u00f5es","funder_award_id":"UIDB/50008/2020","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G3491971210","display_name":null,"funder_award_id":"COMPETE 2020","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G3550249319","display_name":null,"funder_award_id":"NORTE 2020","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G3838636553","display_name":null,"funder_award_id":"-01-0247-","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G3850789590","display_name":null,"funder_award_id":"50008","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G4176497637","display_name":null,"funder_award_id":"COMPETE 2020","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G4423752522","display_name":null,"funder_award_id":"POCI-01-0247-FEDER-045915","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G4455079242","display_name":null,"funder_award_id":"POCI-01-","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G5224927334","display_name":null,"funder_award_id":"POCI-01-","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G6329845220","display_name":null,"funder_award_id":"UIDB/50008/2020","funder_id":"https://openalex.org/F4320335322","funder_display_name":"European Regional Development Fund"},{"id":"https://openalex.org/G8274051256","display_name":null,"funder_award_id":"NORTE 2020","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"},{"id":"https://openalex.org/G8559185249","display_name":null,"funder_award_id":"POCI-01-0247","funder_id":"https://openalex.org/F4320334779","funder_display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia"}],"funders":[{"id":"https://openalex.org/F4320319180","display_name":"Carnegie Mellon Portugal","ror":null},{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"},{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3107975486.pdf","grobid_xml":"https://content.openalex.org/works/W3107975486.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1787224781","https://openalex.org/W1849277567","https://openalex.org/W2064675550","https://openalex.org/W2102099143","https://openalex.org/W2129661061","https://openalex.org/W2129888542","https://openalex.org/W2133656308","https://openalex.org/W2169393322","https://openalex.org/W2282821441","https://openalex.org/W2396881363","https://openalex.org/W2420245003","https://openalex.org/W2517259736","https://openalex.org/W2587529872","https://openalex.org/W2605409611","https://openalex.org/W2745673637","https://openalex.org/W2752194699","https://openalex.org/W2897007327","https://openalex.org/W2950768109","https://openalex.org/W2964199361","https://openalex.org/W2970726176","https://openalex.org/W3016032135","https://openalex.org/W6687502627","https://openalex.org/W6728031117","https://openalex.org/W6748883668","https://openalex.org/W6764072591"],"related_works":[],"abstract_inverted_index":{"Although":[0],"recurrent":[1,29],"neural":[2],"networks":[3],"(RNNs)":[4],"are":[5],"state-of-the-art":[6],"in":[7],"numerous":[8],"sequential":[9,40],"decision-making":[10],"tasks,":[11],"there":[12],"has":[13],"been":[14],"little":[15],"research":[16],"on":[17,151,167],"explaining":[18],"their":[19],"predictions.":[20],"In":[21],"this":[22],"work,":[23],"we":[24,55],"present":[25],"TimeSHAP,":[26],"a":[27,58,85],"model-agnostic":[28],"explainer":[30],"that":[31,61],"builds":[32],"upon":[33],"KernelSHAP":[34],"and":[35,46,71,94,107],"extends":[36],"it":[37],"to":[38,64,80,126,153,163],"the":[39,72,82,102,130,141,156],"domain.":[41],"TimeSHAP":[42,79],"computes":[43],"feature-,":[44],"timestep-,":[45],"cell-level":[47],"attributions.":[48,76],"As":[49],"sequences":[50,122],"may":[51],"be":[52,124],"arbitrarily":[53],"long,":[54],"further":[56],"propose":[57],"pruning":[59],"method":[60],"is":[62],"shown":[63],"dramatically":[65],"decrease":[66],"both":[67],"its":[68,75,99],"computational":[69],"cost":[70],"variance":[73],"of":[74,84,129,146,155],"We":[77],"use":[78],"explain":[81],"predictions":[83,148],"real-world":[86],"bank":[87],"account":[88,117],"takeover":[89],"fraud":[90,112],"detection":[91],"RNN":[92],"model,":[93],"draw":[95],"key":[96],"insights":[97],"from":[98],"explanations:":[100],"i)":[101],"model":[103],"identifies":[104],"important":[105],"features":[106],"events":[108,135],"aligned":[109],"with":[110],"what":[111],"analysts":[113],"consider":[114],"cues":[115],"for":[116,172],"takeover;":[118],"ii)":[119],"positive":[120,147,170],"predicted":[121],"can":[123],"pruned":[125],"only":[127,149],"10%":[128],"original":[131],"length,":[132],"as":[133],"older":[134,173],"have":[136],"residual":[137],"attribution":[138,162],"values;":[139],"iii)":[140],"most":[142],"recent":[143],"input":[144],"event":[145],"contributes":[150],"average":[152],"41%":[154],"model's":[157],"score;":[158],"iv)":[159],"notably":[160],"high":[161],"client's":[164],"age,":[165],"upheld":[166],"higher":[168],"false":[169],"rates":[171],"clients.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":16},{"year":2025,"cited_by_count":40},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-06T08:01:05.025921","created_date":"2020-12-07T00:00:00"}