{"id":"https://openalex.org/W4318148039","doi":"https://doi.org/10.1109/bigdata55660.2022.10020895","title":"Shape-based Evaluation of Epidemic Forecasts","display_name":"Shape-based Evaluation of Epidemic Forecasts","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148039","doi":"https://doi.org/10.1109/bigdata55660.2022.10020895"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020895","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.04035","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002594174","display_name":"Ajitesh Srivastava","orcid":"https://orcid.org/0000-0002-8706-5717"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ajitesh Srivastava","raw_affiliation_strings":["University of Southern California, Los Angeles, United States of America"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States of America","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066902158","display_name":"Satwant Singh","orcid":"https://orcid.org/0000-0001-8838-5723"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Satwant Singh","raw_affiliation_strings":["University of Southern California, Los Angeles, United States of America"],"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States of America","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100379117","display_name":"Fiona Lee","orcid":"https://orcid.org/0000-0003-4376-7503"},"institutions":[],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fiona Lee","raw_affiliation_strings":["Stanford Online High School, United States of America"],"affiliations":[{"raw_affiliation_string":"Stanford Online High School, United States of America","institution_ids":[]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.088,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.669771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":83,"max":85},"biblio":{"volume":null,"issue":null,"first_page":"1701","last_page":"1710"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9985,"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":0.9985,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9721,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9629,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.7371312},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.57070285},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.41595238}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.7371312},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.58170134},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.57070285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.55222917},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.52900535},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5131834},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.41595238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3299536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3030266},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2682802},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22292292},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07341105},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020895","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.04035","pdf_url":"https://arxiv.org/pdf/2209.04035","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2209.04035","pdf_url":"https://arxiv.org/pdf/2209.04035","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.87}],"grants":[{"funder":"https://openalex.org/F4320332162","funder_display_name":"Centers for Disease Control and Prevention","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":7,"referenced_works":["https://openalex.org/W2025720061","https://openalex.org/W2029438113","https://openalex.org/W2098502158","https://openalex.org/W2105119576","https://openalex.org/W2183936775","https://openalex.org/W3031381838","https://openalex.org/W4223487063"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W4255628145","https://openalex.org/W4234808182","https://openalex.org/W2809151339","https://openalex.org/W2387011115","https://openalex.org/W2382043075","https://openalex.org/W2360673138","https://openalex.org/W2348623473","https://openalex.org/W2216913934","https://openalex.org/W1981618449"],"abstract_inverted_index":{"Infectious":[0],"disease":[1],"forecasting":[2],"for":[3,65,245],"ongoing":[4],"epidemics":[5],"has":[6],"been":[7],"traditionally":[8],"performed,":[9],"communicated,":[10],"and":[11,20,26,176,194,205,248],"evaluated":[12],"as":[13,111,222],"numerical":[14,36,127],"targets":[15,37],"\u2013":[16,64],"1,":[17],"2,":[18],"3,":[19],"4":[21],"week":[22],"ahead":[23],"cases,":[24],"deaths,":[25],"hospitalizations.":[27],"While":[28],"there":[29,48],"is":[30,49,84,87,184,234],"great":[31],"value":[32,51],"in":[33,52,191],"predicting":[34],"these":[35],"to":[38,91,94,139,173,186,236],"assess":[39],"the":[40,43,54,59,62,68,98,103,126,140,143,148,160,189,216,223,239,242,254,264,267],"burden":[41],"of":[42,58,61,108,118,125,142,147,150,208,219,225,241,263,266],"disease,":[44],"we":[45,89,121,200],"argue":[46],"that":[47,156,162,165,181,231,253],"also":[50,214,251],"communicating":[53],"future":[55,243],"trend":[56,244],"(description":[57],"shape)":[60],"epidemic":[63],"instance,":[66],"if":[67],"cases":[69,193,247],"will":[70],"remain":[71],"flat":[72],"o":[73],"r":[74],"a":[75,112,123,130,206,260],"s":[76,79],"urge":[77],"i":[78],"expected.":[80],"To":[81],"ensure":[82],"what":[83],"being":[85],"communicated":[86],"useful":[88],"need":[90],"be":[92],"able":[93,185,235],"evaluate":[95],"how":[96],"well":[97],"predicted":[99],"shape":[100,144,240],"matches":[101],"with":[102,145],"ground":[104],"truth":[105],"shape.":[106],"Instead":[107],"treating":[109],"this":[110,134,157,198,232],"classification":[113],"problem":[114],"(":[115],"one":[116,146,166],"out":[117],"n":[119],"shapes),":[120],"define":[122,201,215],"transformation":[124],"forecasts":[128],"into":[129],"\"shapelet\"-space":[131],"representation.":[132],"In":[133],"representation,":[135,199],"each":[136,174],"dimension":[137],"corresponds":[138],"similarity":[141],"shapes":[149,164],"interest":[151],"(a":[152],"shapelet).":[153],"We":[154,179,213,229,250],"prove":[155],"representation":[158,183],"satisfies":[159],"property":[161],"two":[163],"would":[167],"consider":[168],"similar":[169],"are":[170],"mapped":[171],"close":[172],"other,":[175],"vice":[177],"versa.":[178],"demonstrate":[180],"our":[182],"reasonably":[187],"capture":[188],"trends":[190],"COVID-19":[192,246],"deaths":[195],"time-series.":[196],"With":[197],"an":[202],"evaluation":[203],"measure":[204,207],"agreement":[209,255],"among":[210],"multiple":[211,220],"models.":[212],"shapelet-space":[217,227],"ensemble":[218,233],"models":[221,257],"mean":[224],"their":[226],"representations.":[228],"show":[230,252],"accurately":[237],"predict":[238],"trends.":[249],"between":[256],"can":[258],"provide":[259],"good":[261],"indicator":[262],"reliability":[265],"forecast.":[268]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4318148039","counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2025-01-09T15:26:34.698741","created_date":"2023-01-26"}