{"id":"https://openalex.org/W4401372624","doi":"https://doi.org/10.1007/s10489-024-05701-w","title":"Fine-scale deep learning model for time series forecasting","display_name":"Fine-scale deep learning model for time series forecasting","publication_year":2024,"publication_date":"2024-08-06","ids":{"openalex":"https://openalex.org/W4401372624","doi":"https://doi.org/10.1007/s10489-024-05701-w"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-024-05701-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05701-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05701-w.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05701-w.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100828650","display_name":"Yuwei Chen","orcid":"https://orcid.org/0009-0007-2353-9745"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Yuwei Chen","raw_affiliation_strings":["University of Technology Sydney, 2007, NSW, Ultimo, Australia"],"raw_orcid":"https://orcid.org/0009-0007-2353-9745","affiliations":[{"raw_affiliation_string":"University of Technology Sydney, 2007, NSW, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395314","display_name":"Wenjing Jia","orcid":"https://orcid.org/0000-0002-0940-3338"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wenjing Jia","raw_affiliation_strings":["University of Technology Sydney, 2007, NSW, Ultimo, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, 2007, NSW, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068325333","display_name":"Qiang Wu","orcid":"https://orcid.org/0009-0001-1010-5733"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Qiang Wu","raw_affiliation_strings":["University of Technology Sydney, 2007, NSW, Ultimo, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, 2007, NSW, Ultimo, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100828650"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":3.1756,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.91724956,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"54","issue":"20","first_page":"10072","last_page":"10083"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991999864578247,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8615705966949463},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6448137164115906},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6439399719238281},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6388967037200928},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5582427978515625},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.5561825633049011},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5254520773887634},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5235492587089539},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5115724205970764},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5097865462303162},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5040966272354126},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47591668367385864},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46929866075515747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45501720905303955},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.434158593416214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8615705966949463},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6448137164115906},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6439399719238281},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6388967037200928},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5582427978515625},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.5561825633049011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5254520773887634},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5235492587089539},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5115724205970764},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5097865462303162},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5040966272354126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47591668367385864},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46929866075515747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45501720905303955},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.434158593416214},{"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/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-024-05701-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05701-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05701-w.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-024-05701-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-024-05701-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-024-05701-w.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320967","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4401372624.pdf"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2052669361","https://openalex.org/W2117014758","https://openalex.org/W2766799878","https://openalex.org/W2799359565","https://openalex.org/W2811507150","https://openalex.org/W2894821558","https://openalex.org/W2905238323","https://openalex.org/W2920972701","https://openalex.org/W2984380206","https://openalex.org/W3000500483","https://openalex.org/W3005448318","https://openalex.org/W3007066689","https://openalex.org/W3016053201","https://openalex.org/W3022643593","https://openalex.org/W3031080090","https://openalex.org/W3046978353","https://openalex.org/W3101467051","https://openalex.org/W3112027083","https://openalex.org/W3132782787","https://openalex.org/W3136156967","https://openalex.org/W3177318507","https://openalex.org/W4288700733","https://openalex.org/W4366275534","https://openalex.org/W4378573904","https://openalex.org/W4380608860","https://openalex.org/W4384202805"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Abstract":[0],"Time":[1],"series":[2,33,133],"data,":[3],"characterized":[4],"by":[5,39],"large":[6],"volumes":[7],"and":[8,47,71,113,127,152],"wide-ranging":[9],"applications,":[10],"requires":[11],"accurate":[12],"predictions":[13],"of":[14,31,56,98,106,143],"future":[15],"values":[16],"based":[17],"on":[18,137],"historical":[19],"data.":[20,77],"Recent":[21],"advancements":[22],"in":[23,28,60,68,75,109,147],"deep":[24],"learning":[25],"models,":[26],"particularly":[27],"the":[29,54,61,99,104,107,110,141,144],"field":[30],"time":[32,62,132],"forecasting,":[34],"have":[35],"shown":[36],"promising":[37],"results":[38,136],"leveraging":[40],"neural":[41],"networks":[42],"to":[43,65,93],"capture":[44],"complex":[45],"patterns":[46,59,151],"dependencies.":[48],"However,":[49],"existing":[50],"models":[51],"often":[52],"overlook":[53],"influence":[55],"short-term":[57],"cyclical":[58,100,111,150],"series,":[63],"leading":[64],"a":[66,85,95],"lag":[67],"capturing":[69,149],"changes":[70],"accurately":[72],"tracking":[73],"fluctuations":[74],"forecast":[76],"To":[78],"overcome":[79],"this":[80,82],"limitation,":[81],"paper":[83],"introduces":[84],"new":[86],"method":[87,120],"that":[88],"utilizes":[89],"an":[90],"interpolation":[91],"technique":[92],"create":[94],"fine-scaled":[96],"representation":[97],"pattern,":[101],"thereby":[102],"alleviating":[103],"impact":[105],"irregularity":[108],"component":[112],"hence":[114],"enhancing":[115],"prediction":[116,154],"accuracy.":[117,155],"The":[118],"proposed":[119,145],"is":[121],"presented":[122],"along":[123],"with":[124],"evaluation":[125],"metrics":[126],"loss":[128],"functions":[129],"suitable":[130],"for":[131],"forecasting.":[134],"Experiment":[135],"benchmark":[138],"datasets":[139],"demonstrate":[140],"effectiveness":[142],"approach":[146],"effectively":[148],"improving":[153]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}