{"id":"https://openalex.org/W4283706581","doi":"https://doi.org/10.1145/3534678.3539394","title":"Multi-Variate Time Series Forecasting on Variable Subsets","display_name":"Multi-Variate Time Series Forecasting on Variable Subsets","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4283706581","doi":"https://doi.org/10.1145/3534678.3539394"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539394","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539394","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539394","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539394","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038562115","display_name":"Jatin Chauhan","orcid":"https://orcid.org/0000-0002-0209-3883"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jatin Chauhan","raw_affiliation_strings":["Google Research, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Google Research, Bangalore, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084091166","display_name":"Aravindan Raghuveer","orcid":"https://orcid.org/0000-0001-5006-4385"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aravindan Raghuveer","raw_affiliation_strings":["Google Research, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Google Research, Bangalore, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011759026","display_name":"Rishi Saket","orcid":"https://orcid.org/0000-0002-0848-6987"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rishi Saket","raw_affiliation_strings":["Google Research, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Google Research, Bangalore, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077772764","display_name":"Jay Nandy","orcid":"https://orcid.org/0000-0001-8559-4810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jay Nandy","raw_affiliation_strings":["Google Research, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Google Research, Bangalore, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009374923","display_name":"Balaraman Ravindran","orcid":"https://orcid.org/0000-0002-5364-7639"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"facility","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Balaraman Ravindran","raw_affiliation_strings":["Indian Institute of Technology, Madras, Chennai, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology, Madras, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5038562115"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0661,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89486937,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"76","last_page":"86"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9947999715805054,"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/inference","display_name":"Inference","score":0.7239168882369995},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.714787483215332},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6846763491630554},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5455883741378784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5259193181991577},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4715421497821808},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4620690941810608},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.45361095666885376},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.44824323058128357},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.4295384883880615}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7239168882369995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.714787483215332},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6846763491630554},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5455883741378784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5259193181991577},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4715421497821808},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4620690941810608},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.45361095666885376},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.44824323058128357},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.4295384883880615},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3534678.3539394","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539394","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539394","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2206.12626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.12626","pdf_url":"https://arxiv.org/pdf/2206.12626","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:10.1145/3534678.3539394","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3534678.3539394","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3534678.3539394","source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283706581.pdf","grobid_xml":"https://content.openalex.org/works/W4283706581.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2065460148","https://openalex.org/W2080731889","https://openalex.org/W2189007323","https://openalex.org/W2552480641","https://openalex.org/W2592016652","https://openalex.org/W2604847698","https://openalex.org/W2792764867","https://openalex.org/W2805378256","https://openalex.org/W2889928394","https://openalex.org/W2890096158","https://openalex.org/W2890686416","https://openalex.org/W2903871660","https://openalex.org/W2912087261","https://openalex.org/W2951565755","https://openalex.org/W2963076818","https://openalex.org/W2963149635","https://openalex.org/W2963358464","https://openalex.org/W2965341826","https://openalex.org/W2997705255","https://openalex.org/W2998702515","https://openalex.org/W3035805403","https://openalex.org/W3080253043","https://openalex.org/W3115533775","https://openalex.org/W3145217269","https://openalex.org/W3169937871","https://openalex.org/W3171958173","https://openalex.org/W3188872815","https://openalex.org/W4206074191","https://openalex.org/W4239448463","https://openalex.org/W4245202262","https://openalex.org/W4287270621","https://openalex.org/W4287649493","https://openalex.org/W4287778736"],"related_works":["https://openalex.org/W1828158523","https://openalex.org/W2049578243","https://openalex.org/W2000145235","https://openalex.org/W2122079181","https://openalex.org/W1985848810","https://openalex.org/W2889939530","https://openalex.org/W2399195672","https://openalex.org/W3121881699","https://openalex.org/W2748838164","https://openalex.org/W2066015000"],"abstract_inverted_index":{"We":[0,98],"formulate":[1],"a":[2,22,100],"new":[3],"inference":[4,36],"task":[5],"in":[6,65,74,94],"the":[7,26,56,75,84,89,95,138,145],"domain":[8,49],"of":[9,25,38,58,62,67,86,88,137,144],"multivariate":[10],"time":[11],"series":[12],"forecasting":[13],"(MTSF),":[14],"called":[15],"Variable":[16],"Subset":[17],"Forecast":[18],"(VSF),":[19],"where":[20],"only":[21,142],"small":[23],"subset":[24],"variables":[27,147],"is":[28,129],"available":[29],"during":[30,35],"inference.":[31],"Variables":[32],"are":[33,148],"absent":[34],"because":[37],"long-term":[39],"data":[40],"loss":[41],"(eg.":[42],"sensor":[43],"failures)":[44],"or":[45],"high":[46],"->":[47],"low-resource":[48],"shift":[50],"between":[51],"train":[52],"/":[53],"test.":[54],"To":[55],"best":[57],"our":[59,127],"knowledge,":[60],"robustness":[61],"MTSF":[63],"models":[64,139],"presence":[66],"such":[68],"failures,":[69],"has":[70],"not":[71],"been":[72],"studied":[73],"literature.":[76],"Through":[77,114],"extensive":[78],"evaluation,":[79],"we":[80,124],"first":[81],"show":[82,125],"that":[83,104,126],"performance":[85,136],"state":[87],"art":[90],"methods":[91],"degrade":[92],"significantly":[93],"VSF":[96],"setting.":[97],"propose":[99],"non-parametric,":[101],"wrapper":[102],"technique":[103,128],"can":[105],"be":[106],"applied":[107],"on":[108],"top":[109],"any":[110],"existing":[111],"forecast":[112,122],"models.":[113],"systematic":[115],"experiments":[116],"across":[117],"4":[118],"datasets":[119],"and":[120],"5":[121],"models,":[123],"able":[130],"to":[131,134],"recover":[132],"close":[133],"95%":[135],"even":[140],"when":[141],"15%":[143],"original":[146],"present.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}