{"id":"https://openalex.org/W2950418200","doi":"https://doi.org/10.1145/3292500.3330896","title":"Modeling Extreme Events in Time Series Prediction","display_name":"Modeling Extreme Events in Time Series Prediction","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2950418200","doi":"https://doi.org/10.1145/3292500.3330896","mag":"2950418200"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","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/A5014216659","display_name":"Daizong Ding","orcid":"https://orcid.org/0000-0002-4722-5229"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Daizong Ding","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101652939","display_name":"Mi Zhang","orcid":"https://orcid.org/0000-0003-3567-3478"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mi Zhang","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074846459","display_name":"Xudong Pan","orcid":"https://orcid.org/0000-0003-1394-0395"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Pan","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052437722","display_name":"Min Yang","orcid":"https://orcid.org/0000-0001-9714-5545"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Yang","raw_affiliation_strings":["Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038668215","display_name":"Xiangnan He","orcid":"https://orcid.org/0000-0001-8472-7992"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangnan He","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5014216659"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":14.8485,"has_fulltext":false,"cited_by_count":146,"citation_normalized_percentile":{"value":0.99178603,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1114","last_page":"1122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9995999932289124,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9995999932289124,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9988999962806702,"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"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9968000054359436,"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/extreme-value-theory","display_name":"Extreme value theory","score":0.7378676533699036},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7300546169281006},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5773804187774658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5185785293579102},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5077515840530396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4968412220478058},{"id":"https://openalex.org/keywords/extreme-weather","display_name":"Extreme weather","score":0.45360761880874634},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4405905604362488},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.42788153886795044},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4118420481681824},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23768118023872375},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.16582605242729187},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11264774203300476},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10768601298332214}],"concepts":[{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.7378676533699036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7300546169281006},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5773804187774658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5185785293579102},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5077515840530396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4968412220478058},{"id":"https://openalex.org/C205537798","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Extreme weather","level":3,"score":0.45360761880874634},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4405905604362488},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.42788153886795044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4118420481681824},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23768118023872375},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.16582605242729187},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11264774203300476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10768601298332214},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.8199999928474426,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1542459729","https://openalex.org/W1601542853","https://openalex.org/W1674341355","https://openalex.org/W1924770834","https://openalex.org/W1991753520","https://openalex.org/W2014268383","https://openalex.org/W2017086456","https://openalex.org/W2037726243","https://openalex.org/W2038878767","https://openalex.org/W2064675550","https://openalex.org/W2092650071","https://openalex.org/W2096765209","https://openalex.org/W2098849981","https://openalex.org/W2099471712","https://openalex.org/W2101113449","https://openalex.org/W2103452139","https://openalex.org/W2110798204","https://openalex.org/W2115733720","https://openalex.org/W2117420919","https://openalex.org/W2122456939","https://openalex.org/W2122616454","https://openalex.org/W2133564696","https://openalex.org/W2135432950","https://openalex.org/W2141394518","https://openalex.org/W2145996966","https://openalex.org/W2157331557","https://openalex.org/W2288074780","https://openalex.org/W2324099123","https://openalex.org/W2508031629","https://openalex.org/W2605070684","https://openalex.org/W2618530766","https://openalex.org/W2739805805","https://openalex.org/W2741097030","https://openalex.org/W2748571654","https://openalex.org/W2750814024","https://openalex.org/W2788167626","https://openalex.org/W2884561390","https://openalex.org/W2893230400","https://openalex.org/W2945827670","https://openalex.org/W2952042565","https://openalex.org/W2963341924","https://openalex.org/W2963703618","https://openalex.org/W2972424502","https://openalex.org/W3015420945","https://openalex.org/W3037881859","https://openalex.org/W3100278010","https://openalex.org/W3121295160","https://openalex.org/W3123329971","https://openalex.org/W3146803896","https://openalex.org/W3152223742","https://openalex.org/W4205598956","https://openalex.org/W4229651066"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2968645206","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W2743832667","https://openalex.org/W2250060982"],"abstract_inverted_index":{"Time":[0],"series":[1,86,174],"prediction":[2,175],"is":[3],"an":[4,162,169],"intensively":[5],"studied":[6],"topic":[7],"in":[8,29,51,105,150,156,210],"data":[9,184],"mining.":[10],"In":[11,65],"spite":[12],"of":[13,24,59,73,77,91,100,109,128,140,189,198],"the":[14,22,57,70,75,89,98,106,120,137,196],"considerable":[15],"improvements,":[16],"recent":[17],"deep":[18,78,101],"learning-based":[19],"methods":[20,103],"overlook":[21],"existence":[23],"extreme":[25,82,141,154,177],"events,":[26],"which":[27],"result":[28],"weak":[30],"performance":[31],"when":[32],"applying":[33],"them":[34],"to":[35,146,152],"real":[36,53,187],"time":[37,85,173],"series.":[38],"Extreme":[39,121,131],"events":[40,83,155],"are":[41],"rare":[42],"and":[43,62,185,191],"random,":[44],"but":[45],"do":[46],"play":[47],"a":[48,125,205],"critical":[49],"role":[50],"many":[52],"applications,":[54],"such":[55],"as":[56],"forecasting":[58],"financial":[60],"crisis":[61],"natural":[63],"disasters.":[64],"this":[66,114],"paper,":[67],"we":[68,94,116,144,167,193,202],"explore":[69],"central":[71],"theme":[72],"improving":[74],"ability":[76],"learning":[79,102],"on":[80,182],"modeling":[81],"for":[84,135,172,208],"prediction.":[87],"Through":[88,179],"lens":[90],"formal":[92],"analysis,":[93],"first":[95],"find":[96],"that":[97],"weakness":[99],"roots":[104],"conventional":[107],"form":[108,127],"quadratic":[110],"loss.":[111],"To":[112],"address":[113],"issue,":[115],"take":[117],"inspirations":[118],"from":[119],"Value":[122,132],"Theory,":[123],"developing":[124],"new":[126],"loss":[129],"called":[130],"Loss":[133],"(EVL)":[134],"detecting":[136],"future":[138],"occurrence":[139],"events.":[142,178],"Furthermore,":[143],"propose":[145],"employ":[147],"Memory":[148],"Network":[149],"order":[151],"memorize":[153],"historical":[157],"records.By":[158],"incorporating":[159],"EVL":[160],"with":[161,176],"adapted":[163],"memory":[164],"network":[165],"module,":[166],"achieve":[168],"end-to-end":[170],"framework":[171,213],"extensive":[180],"experiments":[181],"synthetic":[183],"two":[186],"datasets":[188],"stock":[190],"climate,":[192],"empirically":[194],"validate":[195],"effectiveness":[197],"our":[199,211],"framework.":[200],"Besides,":[201],"also":[203],"provide":[204],"proper":[206],"choice":[207],"hyper-parameters":[209],"proposed":[212],"by":[214],"conducting":[215],"several":[216],"additional":[217],"experiments.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":26},{"year":2022,"cited_by_count":28},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":19},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2025-10-10T00:00:00"}
