{"id":"https://openalex.org/W4206610455","doi":"https://doi.org/10.1109/bigdata52589.2021.9671636","title":"Temporal and Spatial Attention Network Model Based Evolution Model for Bulk Commodity Price Fluctuation Risk","display_name":"Temporal and Spatial Attention Network Model Based Evolution Model for Bulk Commodity Price Fluctuation Risk","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206610455","doi":"https://doi.org/10.1109/bigdata52589.2021.9671636"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671636","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5004848485","display_name":"Wenjing Zhang","orcid":"https://orcid.org/0000-0002-2046-3948"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhang Wenjing","raw_affiliation_strings":["School of Information Management, Beijing Information Science & Technology University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Management, Beijing Information Science & Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101897965","display_name":"Gang Zhao","orcid":"https://orcid.org/0000-0003-1194-3226"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Gang","raw_affiliation_strings":["School of Information Management, Beijing Information Science & Technology University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Management, Beijing Information Science & Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004848485"],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":1.5314,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83021583,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3284","last_page":"3289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11059","display_name":"Market Dynamics and Volatility","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9296000003814697,"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/T10438","display_name":"Energy, Environment, Economic Growth","score":0.9143000245094299,"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/volatility","display_name":"Volatility (finance)","score":0.828328013420105},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.6339824795722961},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5394291877746582},{"id":"https://openalex.org/keywords/supply-and-demand","display_name":"Supply and demand","score":0.4378974437713623},{"id":"https://openalex.org/keywords/commodity-market","display_name":"Commodity market","score":0.42899060249328613},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.4178960919380188},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.39562752842903137},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.21924394369125366}],"concepts":[{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.828328013420105},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6339824795722961},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5394291877746582},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.4378974437713623},{"id":"https://openalex.org/C2776667075","wikidata":"https://www.wikidata.org/wiki/Q2922469","display_name":"Commodity market","level":2,"score":0.42899060249328613},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.4178960919380188},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.39562752842903137},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.21924394369125366},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671636","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2615053303","https://openalex.org/W2784195128","https://openalex.org/W2791899234","https://openalex.org/W2908754279","https://openalex.org/W2911468715","https://openalex.org/W2925042978","https://openalex.org/W2944361101","https://openalex.org/W2996644132","https://openalex.org/W2997848713"],"related_works":["https://openalex.org/W2291973775","https://openalex.org/W2973880684","https://openalex.org/W2172176281","https://openalex.org/W2753588648","https://openalex.org/W2013499623","https://openalex.org/W4390848234","https://openalex.org/W2259505266","https://openalex.org/W2348509156","https://openalex.org/W3125126082","https://openalex.org/W2499273474"],"abstract_inverted_index":{"Various":[0],"factors":[1],"have":[2],"a":[3,39,133],"dynamic":[4],"and":[5,19,31,57,80,84,104,120,169],"nonlinear":[6],"influence":[7],"on":[8,44],"the":[9,32,51,73,88,96,115,126,143,150,158,167],"price":[10,54,58,85,99,129,164,174],"fluctuation":[11,59,100,175],"of":[12,98,136,172],"bulk":[13,52],"commodities,":[14],"to":[15,49,71,94,113,131],"illustrate,":[16],"market":[17,78],"supply":[18,79],"demand,":[20,81],"raw":[21],"material":[22],"prices,":[23,26,30],"downstream":[24],"product":[25],"seasonal":[27],"factors,":[28],"international":[29],"macroeconomic":[33,82],"environment.":[34],"This":[35,140],"paper":[36,141],"sets":[37],"up":[38],"risk":[40,60],"evolution":[41,97],"model":[42,159],"based":[43],"Graph":[45],"Multi-Attention":[46],"Network":[47],"(GMAN)":[48],"enhance":[50],"commodity":[53],"volatility":[55,130],"prediction":[56],"measurement.":[61],"Node2vec,":[62],"applied":[63,112],"as":[64],"graph":[65],"representations":[66],"learning":[67],"algorithm,":[68],"is":[69,92,111],"used":[70],"map":[72],"relationship":[74,117],"between":[75,118],"geographic":[76],"location,":[77],"environment,":[83],"volatility.":[86],"Furthermore,":[87],"spatio-temporal":[89],"attention":[90,109],"mechanism":[91],"exerted":[93],"analyze":[95],"combining":[101],"spatial":[102],"elements":[103],"time":[105,122,138],"series.":[106],"A":[107],"transform":[108],"layer":[110],"simulate":[114],"direct":[116],"historical":[119,128],"future":[121,137],"steps.":[123,139],"It":[124],"converts":[125],"encoded":[127],"generate":[132],"sequential":[134],"representation":[135],"utilizes":[142],"soybean":[144,163,173],"electronic":[145],"trading":[146],"dataset":[147],"provided":[148],"by":[149],"demonstration":[151],"unit.":[152],"The":[153],"experimental":[154],"results":[155],"show":[156],"that":[157],"can":[160],"effectively":[161],"predict":[162],"volatility,":[165],"influencing":[166],"assessment":[168],"early":[170],"warning":[171],"risk.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}