{"id":"https://openalex.org/W3007000062","doi":"https://doi.org/10.1109/bigdata47090.2019.9006143","title":"Multi-step Forecasting via Multi-task Learning","display_name":"Multi-step Forecasting via Multi-task Learning","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007000062","doi":"https://doi.org/10.1109/bigdata47090.2019.9006143","mag":"3007000062"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5073858605","display_name":"Shayan Jawed","orcid":"https://orcid.org/0009-0001-9130-8208"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Shayan Jawed","raw_affiliation_strings":["Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053107270","display_name":"Ahmed Nabih Zaki Rashed","orcid":"https://orcid.org/0000-0002-5338-1623"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ahmed Rashed","raw_affiliation_strings":["Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039470755","display_name":"Lars Schmidt-Thieme","orcid":"https://orcid.org/0000-0001-5729-6023"},"institutions":[{"id":"https://openalex.org/I155765044","display_name":"University of Hildesheim","ror":"https://ror.org/02f9det96","country_code":"DE","type":"education","lineage":["https://openalex.org/I155765044"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Schmidt-Thieme","raw_affiliation_strings":["Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information Systems and Machine Learning Lab, University of Hildesheim Hildesheim, Germany","institution_ids":["https://openalex.org/I155765044"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1669,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49260454,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"125","issue":null,"first_page":"790","last_page":"799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9986000061035156,"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":0.9986000061035156,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9962999820709229,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9936000108718872,"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/computer-science","display_name":"Computer science","score":0.7692947387695312},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6377627849578857},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6317617893218994},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.6167998909950256},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6120121479034424},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6029742360115051},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6010228991508484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5919126272201538},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.4758871793746948},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4690535366535187},{"id":"https://openalex.org/keywords/horizon","display_name":"Horizon","score":0.4358166456222534},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4205475449562073},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12664514780044556}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692947387695312},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6377627849578857},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6317617893218994},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.6167998909950256},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6120121479034424},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6029742360115051},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6010228991508484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5919126272201538},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.4758871793746948},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4690535366535187},{"id":"https://openalex.org/C159176650","wikidata":"https://www.wikidata.org/wiki/Q43261","display_name":"Horizon","level":2,"score":0.4358166456222534},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4205475449562073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12664514780044556},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:3191240457","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002240711523432","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W777046221","https://openalex.org/W1547333707","https://openalex.org/W1896424170","https://openalex.org/W2014928429","https://openalex.org/W2058398751","https://openalex.org/W2067688816","https://openalex.org/W2093968099","https://openalex.org/W2097998348","https://openalex.org/W2101234009","https://openalex.org/W2109418752","https://openalex.org/W2130942839","https://openalex.org/W2159514083","https://openalex.org/W2168156818","https://openalex.org/W2174647409","https://openalex.org/W2295072214","https://openalex.org/W2295598076","https://openalex.org/W2432541215","https://openalex.org/W2481648081","https://openalex.org/W2519091744","https://openalex.org/W2591927543","https://openalex.org/W2613718673","https://openalex.org/W2624871570","https://openalex.org/W2784720878","https://openalex.org/W2895979207","https://openalex.org/W2913340405","https://openalex.org/W2914788946","https://openalex.org/W2949382160","https://openalex.org/W2963677766","https://openalex.org/W2963722008","https://openalex.org/W2963877604","https://openalex.org/W2963906196","https://openalex.org/W3101973032","https://openalex.org/W3102476541","https://openalex.org/W3123096029","https://openalex.org/W3123760665","https://openalex.org/W4312787320","https://openalex.org/W6620707391","https://openalex.org/W6622444566","https://openalex.org/W6632795786","https://openalex.org/W6639495226","https://openalex.org/W6674385629","https://openalex.org/W6675354045","https://openalex.org/W6679436768","https://openalex.org/W6718046261","https://openalex.org/W6734815144","https://openalex.org/W6738279954","https://openalex.org/W6739365718","https://openalex.org/W6748332148","https://openalex.org/W6759365731","https://openalex.org/W6785528530"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W2623658258","https://openalex.org/W2143413548","https://openalex.org/W1969219540","https://openalex.org/W2370459448","https://openalex.org/W2105067402"],"abstract_inverted_index":{"Multi-task":[0],"learning":[1,16,49,54,131],"is":[2],"an":[3,46],"established":[4],"approach":[5],"for":[6,40,113,129],"improving":[7],"the":[8,18,53,79,99,126,130],"generalization":[9],"of":[10,20,60,81,125,138,152],"a":[11,28,123,159],"model.":[12],"We":[13],"explore":[14],"multi-task":[15,48],"in":[17,45,147,164],"context":[19],"time":[21,88],"series":[22,35,64,89,144],"forecasting.":[23],"Specifically,":[24],"we":[25,121],"look":[26],"into":[27],"multivariate":[29],"setting":[30],"where":[31,52],"main":[32,61,141],"and":[33,62,83,145,154],"auxiliary":[34,63,143],"are":[36],"to":[37,77,135,140],"be":[38,105],"forecasted":[39],"multi-step":[41,93],"ahead.":[42],"This":[43],"results":[44,163],"interesting":[47],"problem":[50],"formulation":[51],"tasks":[55,132],"come":[56],"from":[57],"future":[58],"horizon":[59],"both.":[65],"Our":[66],"proposed":[67],"method":[68],"relies":[69],"firstly":[70],"on":[71,110],"enumerating":[72],"multiple":[73],"Convolutional":[74],"network":[75],"architectures":[76],"balance":[78],"number":[80,151],"shared":[82,153],"non-shared":[84,155],"layers":[85,156],"between":[86],"different":[87,107],"tasks.":[90],"Also,":[91],"as":[92],"strategies":[94],"minimize":[95],"forecast":[96],"errors":[97],"over":[98,167],"complete":[100],"horizon,":[101],"loss":[102],"functions":[103],"would":[104],"at":[106],"scales":[108],"based":[109],"model":[111],"uncertainty":[112],"near":[114],"versus":[115],"distant":[116],"future.":[117,148],"For":[118],"this":[119],"reason":[120],"propose":[122],"factorization":[124],"weight":[127],"vector":[128],"with":[133,158,172],"respect":[134],"their":[136],"categorization":[137],"belonging":[139],"or":[142],"index":[146],"An":[149],"optimal":[150],"together":[157],"novel":[160],"weighted":[161],"loss,":[162],"superior":[165],"performance":[166],"2":[168],"real-world":[169],"datasets":[170],"compared":[171],"several":[173],"baselines.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}