{"id":"https://openalex.org/W4412877197","doi":"https://doi.org/10.1145/3711896.3736854","title":"Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning","display_name":"Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877197","doi":"https://doi.org/10.1145/3711896.3736854"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3736854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736854","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736854","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037786601","display_name":"Minbo Ma","orcid":"https://orcid.org/0000-0001-6098-111X"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"FernUniversit\u00e4t in Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]},{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN","DE"],"is_corresponding":true,"raw_author_name":"Minbo Ma","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, Sichuan, China and FernUniversit\u00e4t in Hagen, Hagen, North Rhine-Westphalia, Germany"],"raw_orcid":"https://orcid.org/0000-0001-6098-111X","affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, Sichuan, China and FernUniversit\u00e4t in Hagen, Hagen, North Rhine-Westphalia, Germany","institution_ids":["https://openalex.org/I120691247","https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kai Tang","orcid":"https://orcid.org/0009-0009-6963-825X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Tang","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0009-0009-6963-825X","affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Huan Li","orcid":"https://orcid.org/0000-0003-0084-1662"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huan Li","raw_affiliation_strings":["Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0084-1662","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016065545","display_name":"Fei Teng","orcid":"https://orcid.org/0000-0001-9535-7245"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Teng","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, Sichuan, China and Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu, Sichuan, China"],"raw_orcid":"https://orcid.org/0000-0001-9535-7245","affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, Sichuan, China and Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101753289","display_name":"Dalin Zhang","orcid":"https://orcid.org/0000-0002-5869-6544"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Dalin Zhang","raw_affiliation_strings":["Aalborg University, Aalborg, Nordjylland, Denmark"],"raw_orcid":"https://orcid.org/0000-0002-5869-6544","affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Nordjylland, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070559820","display_name":"Tianrui Li","orcid":"https://orcid.org/0000-0001-7780-104X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianrui Li","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, Sichuan, China and Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-7780-104X","affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, Sichuan, China and Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5037786601"],"corresponding_institution_ids":["https://openalex.org/I120691247","https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21793746,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2054","last_page":"2065"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9955999851226807,"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"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9922000169754028,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9857000112533569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-variate","display_name":"Random variate","score":0.7918174266815186},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6563384532928467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6331747770309448},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.606850266456604},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5781476497650146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4131508469581604},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.2842947542667389},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2728317081928253},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2515101730823517},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20422253012657166},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11251473426818848}],"concepts":[{"id":"https://openalex.org/C141547133","wikidata":"https://www.wikidata.org/wiki/Q7291996","display_name":"Random variate","level":3,"score":0.7918174266815186},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6563384532928467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6331747770309448},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.606850266456604},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5781476497650146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4131508469581604},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2842947542667389},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2728317081928253},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2515101730823517},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20422253012657166},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11251473426818848},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3736854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736854","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/42f11bca-83f4-4505-affc-737fef12c4c6","is_oa":true,"landing_page_url":"https://vbn.aau.dk/da/publications/42f11bca-83f4-4505-affc-737fef12c4c6","pdf_url":"https://arxiv.org/pdf/2502.15296","source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ma, M, Tang, K, Li, H, Teng, F, Zhang, D & Li, T 2025, Beyond Fixed Variables : Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning. in KDD 2025 : Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2, pp. 2054-2065, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2025, Toronto, Canada, 03/08/2025. https://doi.org/10.1145/3711896.3736854","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1145/3711896.3736854","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3736854","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3736854","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1271771683","display_name":null,"funder_award_id":"62272398","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8811881147","display_name":null,"funder_award_id":"U2468207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877197.pdf","grobid_xml":"https://content.openalex.org/works/W4412877197.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1971402834","https://openalex.org/W2528639018","https://openalex.org/W2559655401","https://openalex.org/W2560647685","https://openalex.org/W2612690371","https://openalex.org/W2965341826","https://openalex.org/W2990138404","https://openalex.org/W3003709258","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3080366021","https://openalex.org/W3107420750","https://openalex.org/W3174686248","https://openalex.org/W3175593095","https://openalex.org/W3177318507","https://openalex.org/W3193056147","https://openalex.org/W3199148273","https://openalex.org/W4211158027","https://openalex.org/W4297192930","https://openalex.org/W4312210066","https://openalex.org/W4382203079","https://openalex.org/W4385567790","https://openalex.org/W4387946814","https://openalex.org/W4388651071","https://openalex.org/W4390437179","https://openalex.org/W4393147362","https://openalex.org/W4393153153","https://openalex.org/W4400909833","https://openalex.org/W6745136726"],"related_works":["https://openalex.org/W4254888883","https://openalex.org/W2784077364","https://openalex.org/W4243513811","https://openalex.org/W2052257784","https://openalex.org/W2035262292","https://openalex.org/W1968059772","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Multivariate":[0],"Time":[1,48],"Series":[2,49],"Forecasting":[3,50],"(MTSF)":[4],"has":[5],"long":[6],"been":[7],"a":[8,16,44,95,102,143,149,157,171],"key":[9],"research":[10],"focus.":[11],"Traditionally,":[12],"these":[13,90],"studies":[14],"assume":[15],"fixed":[17],"number":[18],"of":[19,40,193,240,259,266],"variables,":[20,67],"but":[21],"in":[22,36,231,268],"real-world":[23,201,269],"applications,":[24],"Cyber-Physical":[25],"Systems":[26],"often":[27],"expand":[28],"as":[29,175],"new":[30,66,103],"sensors":[31],"are":[32],"deployed,":[33],"increasing":[34],"variables":[35,76],"MTSF.":[37],"In":[38],"light":[39],"this,":[41],"we":[42,92,147,195],"introduce":[43,148],"novel":[45,150],"task,":[46],"Expanding-variate":[47],"(EVTSF).":[51],"This":[52],"task":[53],"presents":[54],"unique":[55],"challenges,":[56,91],"specifically":[57],"(1)":[58],"handling":[59,232],"inconsistent":[60,109],"data":[61,80,110],"shapes":[62],"caused":[63],"by":[64,123],"adding":[65],"and":[68,167,170,206],"(2)":[69],"addressing":[70],"imbalanced":[71],"spatio-temporal":[72,97,117],"learning,":[73],"where":[74],"expanding":[75,187,233,244,261],"have":[77],"limited":[78],"observed":[79],"due":[81],"to":[82,106,160,178,182],"the":[83,108,115,125,129,133,180,186,191,243,264],"necessity":[84],"for":[85,219],"timely":[86],"operation.":[87],"To":[88,189],"address":[89],"propose":[93],"STEV,":[94,194,236],"flexible":[96],"forecasting":[98],"framework.":[99],"STEV":[100,225,267],"includes":[101],"Flat":[104],"Scheme":[105],"tackle":[107],"shape":[111],"issue,":[112],"which":[113],"extends":[114],"graph-based":[116],"modeling":[118],"architecture":[119],"into":[120],"1D":[121],"space":[122],"flattening":[124],"2D":[126],"samples":[127],"along":[128],"variable":[130],"dimension,":[131],"making":[132],"model":[134],"variable-scale-agnostic":[135],"while":[136],"still":[137],"preserving":[138],"dynamic":[139],"spatial":[140],"correlations":[141],"through":[142],"holistic":[144],"graph.":[145],"Additionally,":[146],"Spatio-temporal":[151],"Focal":[152],"Learning":[153],"strategy":[154],"that":[155,224],"incorporates":[156],"negative":[158],"filter":[159],"resolve":[161],"potential":[162,211],"conflicts":[163],"between":[164],"contrastive":[165,173],"learning":[166],"graph":[168],"representation,":[169],"focal":[172],"loss":[174],"its":[176,228],"core":[177],"guide":[179],"framework":[181],"focus":[183],"on":[184,199,247],"optimizing":[185],"variables.":[188,234],"evaluate":[190],"effectiveness":[192],"benchmark":[196],"EVTSF":[197],"performance":[198],"three":[200,210],"datasets":[202],"from":[203],"various":[204,260],"domains":[205],"compare":[207],"it":[208],"against":[209],"solutions":[212],"employing":[213],"state-of-the-art":[214],"(SOTA)":[215],"MTSF":[216,251],"models":[217,252],"tailored":[218],"EVSTF.":[220],"Experimental":[221],"results":[222],"show":[223],"significantly":[226],"outperforms":[227],"competitors,":[229],"especially":[230],"Notably,":[235],"with":[237,249,254],"only":[238],"5%":[239],"observations":[241],"during":[242],"period,":[245],"is":[246],"par":[248],"SOTA":[250],"trained":[253],"complete":[255],"data.":[256],"Further":[257],"exploration":[258],"scenarios":[262],"underscores":[263],"generalizability":[265],"applications.":[270]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}