{"id":"https://openalex.org/W2964716450","doi":"https://doi.org/10.24963/ijcai.2019/932","title":"Explainable Deep Neural Networks for Multivariate Time Series Predictions","display_name":"Explainable Deep Neural Networks for Multivariate Time Series Predictions","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964716450","doi":"https://doi.org/10.24963/ijcai.2019/932","mag":"2964716450"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/932","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/932","pdf_url":"https://www.ijcai.org/proceedings/2019/0932.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0932.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037727287","display_name":"Roy Assaf","orcid":"https://orcid.org/0000-0002-6132-8347"},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Roy Assaf","raw_affiliation_strings":["IBM Research, Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich","institution_ids":["https://openalex.org/I4210126328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069005357","display_name":"Anika Schumann","orcid":null},"institutions":[{"id":"https://openalex.org/I4210126328","display_name":"IBM Research - Zurich","ror":"https://ror.org/02js37d36","country_code":"CH","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210126328"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Anika Schumann","raw_affiliation_strings":["IBM Research, Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research, Zurich","institution_ids":["https://openalex.org/I4210126328"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210126328"],"apc_list":null,"apc_paid":null,"fwci":6.336,"has_fulltext":false,"cited_by_count":99,"citation_normalized_percentile":{"value":0.97369188,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6488","last_page":"6490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9922999739646912,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9922999739646912,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9783999919891357,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7456945180892944},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7188652753829956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6344566345214844},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6291929483413696},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6224234700202942},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5888299942016602},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5767768621444702},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.534666121006012},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5299606919288635},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4753446578979492},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47034671902656555},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4303460717201233},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.43018242716789246},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4272534251213074},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4156651496887207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13859400153160095},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13761818408966064}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7456945180892944},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7188652753829956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6344566345214844},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6291929483413696},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6224234700202942},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5888299942016602},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5767768621444702},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.534666121006012},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5299606919288635},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4753446578979492},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47034671902656555},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4303460717201233},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.43018242716789246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4272534251213074},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4156651496887207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13859400153160095},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13761818408966064},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.24963/ijcai.2019/932","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/932","pdf_url":"https://www.ijcai.org/proceedings/2019/0932.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:3843273","is_oa":true,"landing_page_url":"https://zenodo.org/record/3843273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/932","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/932","pdf_url":"https://www.ijcai.org/proceedings/2019/0932.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8799999952316284}],"awards":[{"id":"https://openalex.org/G3105035837","display_name":null,"funder_award_id":"EU-H2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G468837769","display_name":null,"funder_award_id":"745625","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"},{"id":"https://openalex.org/G4716868515","display_name":"Reliable OM decision tools and strategies for high LCoE reduction on Offshore wind","funder_award_id":"745625","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4937468798","display_name":null,"funder_award_id":"H2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G495187929","display_name":null,"funder_award_id":"EU\u2010H2020","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320335238","display_name":"Staatssekretariat f\u00fcr Bildung, Forschung und Innovation","ror":"https://ror.org/01kw63t33"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964716450.pdf","grobid_xml":"https://content.openalex.org/works/W2964716450.grobid-xml"},"referenced_works_count":10,"referenced_works":["https://openalex.org/W119403003","https://openalex.org/W1850325679","https://openalex.org/W1997102766","https://openalex.org/W2097117768","https://openalex.org/W2508380041","https://openalex.org/W2807015674","https://openalex.org/W2891612330","https://openalex.org/W2892035503","https://openalex.org/W2953073956","https://openalex.org/W2962858109"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W2324780611","https://openalex.org/W2280920478","https://openalex.org/W1570805059","https://openalex.org/W2357266745","https://openalex.org/W1578824628","https://openalex.org/W2361261277"],"abstract_inverted_index":{"We":[0,52,124],"demonstrate":[1,125],"that":[2,122],"CNN":[3],"deep":[4],"neural":[5,58],"networks":[6],"can":[7],"not":[8],"only":[9],"be":[10],"used":[11,89],"for":[12,23,30,40,74,78,90,100,121,128,139],"making":[13],"predictions":[14,36],"based":[15,72],"on":[16],"multivariate":[17],"time":[18,81,96,110],"series":[19],"data,":[20],"but":[21],"also":[22],"explaining":[24,91,107,140],"these":[25,141],"predictions.":[26,142],"This":[27,66],"is":[28,50],"important":[29,120],"a":[31,54,101],"number":[32],"of":[33,116,134],"applications":[34],"where":[35],"are":[37,87,98],"the":[38,47,80,84,113,130],"basis":[39],"decisions":[41],"and":[42,83,138],"actions.":[43],"Hence,":[44],"confidence":[45],"in":[46],"prediction":[48],"result":[49],"crucial.":[51],"design":[53],"two":[55],"stage":[56],"convolutional":[57],"network":[59],"architecture":[60],"which":[61,92,95,109],"uses":[62],"particular":[63],"kernel":[64],"sizes.":[65],"allows":[67],"us":[68],"to":[69],"utilise":[70],"gradient":[71],"techniques":[73],"generating":[75],"saliency":[76],"maps":[77],"both":[79],"dimension":[82],"features.":[85],"These":[86],"then":[88],"features":[93,118],"during":[94,108],"interval":[97],"responsible":[99],"given":[102],"prediction,":[103],"as":[104,106],"well":[105],"intervals":[111],"was":[112],"joint":[114],"contribution":[115],"all":[117],"most":[119],"prediction.":[123],"our":[126],"approach":[127],"predicting":[129],"average":[131],"energy":[132],"production":[133],"photovoltaic":[135],"power":[136],"plants":[137]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}