{"id":"https://openalex.org/W4404479923","doi":"https://doi.org/10.1145/3686081.3686085","title":"Deep Learning Techniques in Time Series Forecasting: a Study on RMB Exchange Rate Forecasting Based on Google Search Data","display_name":"Deep Learning Techniques in Time Series Forecasting: a Study on RMB Exchange Rate Forecasting Based on Google Search Data","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4404479923","doi":"https://doi.org/10.1145/3686081.3686085"},"language":"en","primary_location":{"id":"doi:10.1145/3686081.3686085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686081.3686085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science & Management","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003395814","display_name":"Qun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qun Xu","raw_affiliation_strings":["Shanghai Dianji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0004-6818-3525","affiliations":[{"raw_affiliation_string":"Shanghai Dianji University, Shanghai, China","institution_ids":["https://openalex.org/I4210156189"]}]},{"author_position":"last","author":{"id":null,"display_name":"Dazhi Cheng","orcid":"https://orcid.org/0009-0009-9067-5047"},"institutions":[{"id":"https://openalex.org/I4210156189","display_name":"Shanghai Dianji University","ror":"https://ror.org/055fene14","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dazhi Cheng","raw_affiliation_strings":["Shanghai Dianji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0009-9067-5047","affiliations":[{"raw_affiliation_string":"Shanghai Dianji University, Shanghai, China","institution_ids":["https://openalex.org/I4210156189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210156189"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29568225,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.8461999893188477,"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":0.8461999893188477,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.7932999730110168,"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":0.7200000286102295,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7124242782592773},{"id":"https://openalex.org/keywords/renminbi","display_name":"Renminbi","score":0.6786961555480957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6400540471076965},{"id":"https://openalex.org/keywords/exchange-rate","display_name":"Exchange rate","score":0.5766108632087708},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5479346513748169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4791921377182007},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4633543789386749},{"id":"https://openalex.org/keywords/economic-forecasting","display_name":"Economic forecasting","score":0.446661114692688},{"id":"https://openalex.org/keywords/technology-forecasting","display_name":"Technology forecasting","score":0.4373050332069397},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3847222924232483},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.357488214969635},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.14796119928359985},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.13277336955070496}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7124242782592773},{"id":"https://openalex.org/C9841139","wikidata":"https://www.wikidata.org/wiki/Q39099","display_name":"Renminbi","level":3,"score":0.6786961555480957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6400540471076965},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.5766108632087708},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5479346513748169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4791921377182007},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4633543789386749},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.446661114692688},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.4373050332069397},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3847222924232483},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.357488214969635},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.14796119928359985},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.13277336955070496},{"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":1,"locations":[{"id":"doi:10.1145/3686081.3686085","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3686081.3686085","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Decision Science & 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":0,"referenced_works":[],"related_works":["https://openalex.org/W2381433509","https://openalex.org/W2006682031","https://openalex.org/W2363286014","https://openalex.org/W2356016147","https://openalex.org/W2369410041","https://openalex.org/W2009558723","https://openalex.org/W2118549504","https://openalex.org/W2371963750","https://openalex.org/W2388397562","https://openalex.org/W4295556373"],"abstract_inverted_index":{"This":[0],"study":[1,133],"is":[2,76,134],"devoted":[3],"to":[4],"exploring":[5],"the":[6,22,26,31,38,56,62,70,74,81,87,100,110,141,153],"application":[7,142],"of":[8,25,58,73,112,140,143,155],"time":[9,144],"series":[10,145],"forecasting":[11,23,40,64,151],"models":[12,146],"in":[13,147],"RMB":[14,101,148],"exchange":[15,102,149],"rate":[16,103,150],"movements,":[17],"and":[18,30,43,115,127,152],"focuses":[19],"on":[20,61,99],"comparing":[21,37],"effects":[24],"traditional":[27],"ARIMA":[28],"model":[29,39,63,75,123,125,128],"deep":[32],"learning-based":[33],"LSTM":[34],"model.":[35],"By":[36],"results":[41,67],"before":[42],"after":[44,79],"adding":[45,80],"Google":[46,82,88],"search":[47,83,89],"index":[48,90],"as":[49,91],"an":[50,92,96],"exogenous":[51,93],"variable,":[52],"this":[53,107,132],"paper":[54,108],"analyzes":[55],"influence":[57],"external":[59],"factors":[60],"accuracy.":[65],"The":[66],"show":[68],"that":[69,86],"prediction":[71],"performance":[72],"significantly":[77],"improved":[78],"index,":[84],"indicating":[85],"variable":[94],"has":[95],"important":[97],"impact":[98],"prediction.":[104],"In":[105,130],"addition,":[106],"discusses":[109],"limitations":[111],"single-model":[113],"research":[114],"proposes":[116],"possible":[117],"directions":[118],"for":[119,136],"future":[120,156],"research,":[121],"including":[122],"selection,":[124],"integration":[126],"interpretation.":[129],"summary,":[131],"instructive":[135],"a":[137],"deeper":[138],"understanding":[139],"direction":[154],"research.":[157]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}