{"id":"https://openalex.org/W3178466602","doi":"https://doi.org/10.1145/3490354.3494387","title":"Visual time series forecasting","display_name":"Visual time series forecasting","publication_year":2021,"publication_date":"2021-11-03","ids":{"openalex":"https://openalex.org/W3178466602","doi":"https://doi.org/10.1145/3490354.3494387","mag":"3178466602"},"language":"en","primary_location":{"id":"doi:10.1145/3490354.3494387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490354.3494387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2011.09052","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043221382","display_name":"Srijan Sood","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Srijan Sood","raw_affiliation_strings":["J. P. Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J. P. Morgan AI Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360190","display_name":"Zhen Zeng","orcid":"https://orcid.org/0000-0003-4383-3590"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhen Zeng","raw_affiliation_strings":["J. P. Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J. P. Morgan AI Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058278967","display_name":"Naftali Cohen","orcid":"https://orcid.org/0000-0002-0550-1539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naftali Cohen","raw_affiliation_strings":["J. P. Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J. P. Morgan AI Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035482777","display_name":"Tucker Balch","orcid":"https://orcid.org/0000-0002-5148-2033"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tucker Balch","raw_affiliation_strings":["J. P. Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J. P. Morgan AI Research","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088276691","display_name":"Manuela Veloso","orcid":"https://orcid.org/0000-0001-6738-238X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manuela Veloso","raw_affiliation_strings":["J. P. Morgan AI Research"],"affiliations":[{"raw_affiliation_string":"J. P. Morgan AI Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043221382"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3875,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.60440237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9660999774932861,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.9660999774932861,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10895","display_name":"Species Distribution and Climate Change","score":0.9406999945640564,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9388999938964844,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7629290819168091},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7236870527267456},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7003253102302551},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5563720464706421},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.5258075594902039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5110862851142883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5092326402664185},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.463172972202301},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4379798471927643},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3739468455314636},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10235235095024109}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7629290819168091},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7236870527267456},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7003253102302551},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5563720464706421},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.5258075594902039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5110862851142883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5092326402664185},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.463172972202301},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4379798471927643},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3739468455314636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10235235095024109},{"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/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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3490354.3494387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490354.3494387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second ACM International Conference on AI in Finance","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2011.09052","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.09052","pdf_url":"https://arxiv.org/pdf/2011.09052","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":"pmh:oai:arXiv.org:2011.09052","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2011.09052","pdf_url":"https://arxiv.org/pdf/2011.09052","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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W70169678","https://openalex.org/W239537540","https://openalex.org/W1156748138","https://openalex.org/W1480376833","https://openalex.org/W1811734137","https://openalex.org/W2031489346","https://openalex.org/W2048665112","https://openalex.org/W2153529786","https://openalex.org/W2162800060","https://openalex.org/W2174424190","https://openalex.org/W2322045482","https://openalex.org/W2471801048","https://openalex.org/W2510850936","https://openalex.org/W2587586954","https://openalex.org/W2611104282","https://openalex.org/W2614634292","https://openalex.org/W2734777338","https://openalex.org/W2811507150","https://openalex.org/W2884745705","https://openalex.org/W2889920518","https://openalex.org/W2894821558","https://openalex.org/W2943227802","https://openalex.org/W2962766617","https://openalex.org/W2963130063","https://openalex.org/W2963507686","https://openalex.org/W2963907301","https://openalex.org/W2970971581","https://openalex.org/W2973226272","https://openalex.org/W2976235793","https://openalex.org/W2995015263","https://openalex.org/W3007103623","https://openalex.org/W3016895873","https://openalex.org/W3037110414","https://openalex.org/W3048383900","https://openalex.org/W3122446649","https://openalex.org/W3130883324","https://openalex.org/W3203509268","https://openalex.org/W4244015108","https://openalex.org/W4288102711","https://openalex.org/W4288279950","https://openalex.org/W4288911636","https://openalex.org/W4293282014","https://openalex.org/W4295312788","https://openalex.org/W4298304654","https://openalex.org/W4399572130"],"related_works":["https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2146461990","https://openalex.org/W4312309719","https://openalex.org/W4391216528","https://openalex.org/W2980748541","https://openalex.org/W4399581288","https://openalex.org/W4313123484"],"abstract_inverted_index":{"Time":[0],"series":[1,77],"forecasting":[2,78,127,154,179],"is":[3,128],"essential":[4],"for":[5,130,136,183],"agents":[6],"to":[7,16,34,51,56,71,79,94,107,156,199],"make":[8],"decisions.":[9],"Traditional":[10],"approaches":[11,177],"rely":[12,26],"on":[13,27],"statistical":[14],"methods":[15],"forecast":[17],"given":[18],"past":[19],"numeric":[20],"values.":[21,109],"In":[22,62],"practice,":[23],"end-users":[24],"often":[25],"visualizations":[28],"such":[29,139],"as":[30,87,105,140,190,192],"charts":[31],"and":[32,90,114,163],"plots":[33],"reason":[35],"about":[36],"their":[37],"forecasts.":[38],"Inspired":[39],"by":[40,46],"practitioners,":[41],"we":[42,65,149],"re-imagine":[43],"the":[44,57,73,96,151,172,184,187,193],"topic":[45],"creating":[47],"a":[48,80,92,164],"novel":[49],"framework":[50],"produce":[52,95],"visual":[53,81,126,153],"forecasts,":[54],"similar":[55],"way":[58],"humans":[59],"intuitively":[60],"do.":[61],"this":[63],"work,":[64],"leverage":[66],"advances":[67],"in":[68,102,178],"deep":[69],"learning":[70],"extend":[72],"field":[74],"of":[75,120,167,174,186],"time":[76],"setting.":[82],"We":[83,110,170],"capture":[84],"input":[85],"data":[86,132,138],"an":[88],"image":[89],"train":[91],"model":[93],"subsequent":[97],"image.":[98],"This":[99],"approach":[100],"results":[101],"predicting":[103],"distributions":[104],"opposed":[106],"pointwise":[108],"examine":[111],"various":[112,158],"synthetic":[113],"real":[115],"datasets":[116],"with":[117],"diverse":[118],"degrees":[119],"complexity.":[121],"Our":[122],"experiments":[123],"show":[124],"that":[125,195],"effective":[129],"cyclic":[131],"but":[133],"somewhat":[134],"less":[135],"irregular":[137],"stock":[141],"price.":[142],"Importantly,":[143],"when":[144],"using":[145],"image-based":[146],"evaluation":[147],"metrics,":[148],"find":[150],"proposed":[152],"method":[155],"outperform":[157],"numerical":[159,165],"baselines,":[160],"including":[161],"ARIMA":[162],"variation":[166],"our":[168],"method.":[169],"demonstrate":[171],"benefits":[173],"incorporating":[175],"vision-based":[176],"tasks":[180],"--":[181],"both":[182],"quality":[185],"forecasts":[188],"produced,":[189],"well":[191],"metrics":[194],"can":[196],"be":[197],"used":[198],"evaluate":[200],"them.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2021-07-19T00:00:00"}