{"id":"https://openalex.org/W2767350339","doi":"https://doi.org/10.1145/3132847.3133109","title":"A Study of Feature Construction for Text-based Forecasting of Time Series Variables","display_name":"A Study of Feature Construction for Text-based Forecasting of Time Series Variables","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2767350339","doi":"https://doi.org/10.1145/3132847.3133109","mag":"2767350339"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3133109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on 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/A5100723437","display_name":"Yiren Wang","orcid":"https://orcid.org/0000-0001-6757-5923"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yiren Wang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006755170","display_name":"Dominic Seyler","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dominic Seyler","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058755530","display_name":"Shubhra Kanti Karmaker Santu","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shubhra Kanti Karmaker Santu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028518494","display_name":"ChengXiang Zhai","orcid":"https://orcid.org/0000-0002-6434-3702"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"ChengXiang Zhai","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100723437"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.1701,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84574654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2347","last_page":"2350"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9987000226974487,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9987000226974487,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9926000237464905,"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":0.7621615529060364},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6596304178237915},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5176659226417542},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5085369348526001},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48721617460250854},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47615739703178406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46839261054992676},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.449290931224823},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4482646882534027}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7621615529060364},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6596304178237915},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5176659226417542},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5085369348526001},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48721617460250854},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47615739703178406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46839261054992676},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.449290931224823},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4482646882534027},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3133109","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3133109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1259090559","https://openalex.org/W1880262756","https://openalex.org/W1952479354","https://openalex.org/W1972062191","https://openalex.org/W1988518729","https://openalex.org/W1995141167","https://openalex.org/W2030005958","https://openalex.org/W2039935421","https://openalex.org/W2094665138","https://openalex.org/W2126267628","https://openalex.org/W2144211451","https://openalex.org/W2246472863","https://openalex.org/W2250629460","https://openalex.org/W2271835578","https://openalex.org/W2296438605","https://openalex.org/W2963324110","https://openalex.org/W3122714990"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W1976205134","https://openalex.org/W2381570729","https://openalex.org/W4248336175","https://openalex.org/W2622688551","https://openalex.org/W2119012848","https://openalex.org/W1990205660","https://openalex.org/W1550175370","https://openalex.org/W4387331850"],"abstract_inverted_index":{"Time":[0],"series":[1,26,49,71,145],"are":[2,9,150],"ubiquitous":[3],"in":[4],"the":[5,43,47,74,91,135],"world":[6],"since":[7],"they":[8,156],"used":[10,76],"to":[11,59,174],"measure":[12],"various":[13],"phenomena":[14],"(e.g.,":[15],"temperature,":[16],"spread":[17],"of":[18,24,46,138,165,179],"a":[19,37,81,95,103,176],"virus,":[20],"sales,":[21],"etc.).":[22],"Forecasting":[23],"time":[25,48,70,131,144,182],"is":[27,36,50],"highly":[28],"beneficial":[29],"(and":[30],"necessary)":[31],"for":[32,69,84,106,120,142],"optimizing":[33],"decisions,":[34],"yet":[35],"very":[38,152],"challenging":[39],"problem;":[40],"using":[41,102],"only":[42],"historical":[44],"values":[45],"often":[51],"insufficient.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56,79,112],"study":[57],"how":[58],"construct":[60],"effective":[61,153],"additional":[62,114],"features":[63,88,115,129,149],"based":[64,89],"on":[65,90,163,186],"related":[66],"text":[67,118,140],"data":[68,104,141],"forecasting.":[72,146],"Besides":[73],"commonly":[75],"n-gram":[77,166],"features,":[78,133],"propose":[80],"general":[82],"strategy":[83],"constructing":[85],"multiple":[86],"topical":[87],"topics":[92,180],"discovered":[93],"by":[94],"topic":[96],"model.":[97],"We":[98,124],"evaluate":[99],"feature":[100,172],"effectiveness":[101],"set":[105],"predicting":[107],"stock":[108,121],"price":[109],"changes":[110],"where":[111],"constructed":[113],"from":[116],"news":[117],"articles":[119],"market":[122],"prediction.":[123],"found":[125],"that:":[126],"1)":[127],"Text-based":[128],"outperform":[130],"series-based":[132],"suggesting":[134],"great":[136],"promise":[137],"leveraging":[139],"improving":[143],"2)":[147],"Topic-based":[148],"not":[151],"stand-alone,":[154],"but":[155],"can":[157],"further":[158],"improve":[159],"performance":[160],"when":[161],"added":[162],"top":[164],"features.":[167],"3)":[168],"The":[169],"best":[170],"topic-based":[171],"appears":[173],"be":[175],"long-term":[177],"aggregation":[178],"over":[181],"with":[183],"high":[184],"weights":[185],"recent":[187],"topics.":[188]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}