{"id":"https://openalex.org/W4416017260","doi":"https://doi.org/10.1145/3746252.3761173","title":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","display_name":"MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416017260","doi":"https://doi.org/10.1145/3746252.3761173"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761173","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025179322","display_name":"Binqing Wu","orcid":"https://orcid.org/0000-0001-8276-0801"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binqing Wu","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0001-8276-0801","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055984231","display_name":"Zongjiang Shang","orcid":"https://orcid.org/0009-0001-8938-7437"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongjiang Shang","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0001-8938-7437","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050808388","display_name":"Jianlong Huang","orcid":"https://orcid.org/0009-0004-5378-159X"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianlong Huang","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0004-5378-159X","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100411139","display_name":"Ling Chen","orcid":"https://orcid.org/0000-0003-1934-5992"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Chen","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0003-1934-5992","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I168879160"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36017784,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3344","last_page":"3354"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.40459999442100525,"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":0.40459999442100525,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.1298999935388565,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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":0.12370000034570694,"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/interpretability","display_name":"Interpretability","score":0.9239000082015991},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5491999983787537},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4925999939441681},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4562999904155731},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4359999895095825},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.40139999985694885},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3765000104904175}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9239000082015991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746999979019165},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5491999983787537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5065000057220459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4945000112056732},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4521999955177307},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C26320393","wikidata":"https://www.wikidata.org/wiki/Q597053","display_name":"Functional dependency","level":3,"score":0.33889999985694885},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761173","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W1988899076","https://openalex.org/W2069929199","https://openalex.org/W2980994438","https://openalex.org/W3177318507","https://openalex.org/W4366377753","https://openalex.org/W4381245607","https://openalex.org/W4382203079","https://openalex.org/W4382239616","https://openalex.org/W4388994345","https://openalex.org/W4393147362","https://openalex.org/W4393153241","https://openalex.org/W4393159606","https://openalex.org/W4393160624","https://openalex.org/W4394802134","https://openalex.org/W4396757566","https://openalex.org/W4399695543","https://openalex.org/W4403577324","https://openalex.org/W4409363435","https://openalex.org/W4409363871"],"related_works":[],"abstract_inverted_index":{"Multi-variate":[0],"time":[1,108],"series":[2],"(MTS)":[3],"forecasting":[4],"is":[5],"crucial":[6],"for":[7,66,114,179],"various":[8],"applications.":[9],"Existing":[10],"methods":[11,29],"have":[12],"shown":[13],"promising":[14],"results":[15,170],"owing":[16],"to":[17,21,39,110,148],"their":[18],"strong":[19],"ability":[20],"capture":[22,40,72,158],"intra-":[23,151],"and":[24,77,80,99,107,126,152,167,181],"inter-variate":[25],"dependencies.":[26],"However,":[27],"these":[28],"often":[30],"overlook":[31],"lead-lag":[32,42,64,73,91,112,121,132,138,154,160],"dependencies":[33,65,113,122],"at":[34,140],"multiple":[35,61,141],"grouping":[36,62,142],"scales,":[37],"failing":[38],"hierarchical":[41,131],"effects":[43,74,161],"in":[44,144],"complex":[45],"systems.":[46],"To":[47],"this":[48],"end,":[49],"we":[50],"propose":[51],"MillGNN,":[52],"a":[53,89,130,145,163],"novel":[54],"graph":[55,92],"neural":[56],"network-based":[57],"method":[58],"that":[59,95,136],"learns":[60],"scale":[63],"MTS":[67,183],"forecasting,":[68,184],"which":[69,117,156],"can":[70,118,157],"comprehensively":[71],"considering":[75],"variate-wise":[76],"group-wise":[78],"dynamics":[79],"decays.":[81],"Specifically,":[82],"MillGNN":[83,178],"introduces":[84],"two":[85],"key":[86],"innovations:":[87],"(1)":[88],"scale-specific":[90],"learning":[93],"module":[94,135],"integrates":[96],"cross-correlation":[97],"coefficients":[98],"dynamic":[100],"decaying":[101],"features":[102],"derived":[103],"from":[104],"real-time":[105],"inputs":[106],"lags":[109],"learn":[111],"each":[115],"scale,":[116],"model":[119],"evolving":[120],"with":[123,162,186],"statistical":[124],"interpretability":[125],"data-driven":[127],"flexibility;":[128],"(2)":[129],"message":[133],"passing":[134],"passes":[137],"messages":[139],"scales":[143],"structured":[146],"way":[147],"simultaneously":[149],"propagate":[150],"inter-scale":[153],"effects,":[155],"multi-scale":[159],"balance":[164],"of":[165,177],"comprehensiveness":[166],"efficiency.":[168],"Experimental":[169],"on":[171],"11":[172],"datasets":[173],"demonstrate":[174],"the":[175],"superiority":[176],"long-term":[180],"short-term":[182],"compared":[185],"16":[187],"state-of-the-art":[188],"methods.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-08T00:00:00"}