{"id":"https://openalex.org/W4416251655","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227953","title":"PSNet: A Multi-Period and Multi-Scale Model for Long-term Time Series Forecasting","display_name":"PSNet: A Multi-Period and Multi-Scale Model for Long-term Time Series Forecasting","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251655","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227953"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5045464395","display_name":"Z G Chen","orcid":"https://orcid.org/0009-0004-3252-6282"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zehang Chen","raw_affiliation_strings":["Xiamen University,School of Informatics,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,School of Informatics,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102974280","display_name":"Licun Dai","orcid":"https://orcid.org/0009-0006-4452-1336"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licun Dai","raw_affiliation_strings":["Xiamen University,School of Informatics,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,School of Informatics,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038729392","display_name":"Fan Lin","orcid":"https://orcid.org/0000-0003-2530-859X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Lin","raw_affiliation_strings":["Xiamen University,School of Informatics,Xiamen,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,School of Informatics,Xiamen,China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045464395"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39117043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4124999940395355,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4124999940395355,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.2994999885559082,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.06480000168085098,"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/time-series","display_name":"Time series","score":0.7111999988555908},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.6884999871253967},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.590399980545044},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.45500001311302185},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.33980000019073486}],"concepts":[{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7111999988555908},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.6884999871253967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6656000018119812},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.590399980545044},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3716000020503998},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32710000872612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3118000030517578},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227953","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":17,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W1969852690","https://openalex.org/W1983883318","https://openalex.org/W2093643917","https://openalex.org/W2116174583","https://openalex.org/W2127604212","https://openalex.org/W2290960045","https://openalex.org/W2503637115","https://openalex.org/W2573587735","https://openalex.org/W2604847698","https://openalex.org/W2897257904","https://openalex.org/W2950678961","https://openalex.org/W2980994438","https://openalex.org/W3019433526","https://openalex.org/W3177318507","https://openalex.org/W4382203079","https://openalex.org/W4382239356"],"related_works":[],"abstract_inverted_index":{"Long-term":[0],"Time":[1],"Series":[2],"Forecasting":[3],"(LTSF)":[4],"aims":[5],"to":[6,35,81,181],"predict":[7],"time":[8,56,95,128],"series":[9,57,96],"data":[10,62],"over":[11],"extended":[12],"future":[13],"horizons.":[14],"In":[15],"recent":[16],"years,":[17],"multi-scale":[18,37,103,139],"mixing":[19,38,101,104,140,154],"and":[20,66,90,102,117,124,151,177],"multi-period":[21,40,100,153],"analysis":[22,41],"have":[23],"gained":[24],"significant":[25],"traction":[26],"in":[27,54,93,127,174],"LTSF":[28,175],"models.":[29],"However,":[30],"existing":[31],"approaches":[32],"often":[33],"fail":[34],"integrate":[36],"with":[39,61],"effectively.":[42,186],"Additionally,":[43],"while":[44],"the":[45,83,106,112,118,131,137,144,152,158,167],"emerging":[46],"cross-period":[47,107],"sparse":[48,108],"forecasting":[49,109],"technique":[50],"has":[51],"shown":[52],"promise":[53],"modeling":[55,145],"periodicity,":[58],"it":[59],"struggles":[60],"characterized":[63],"by":[64,86],"multiple":[65,115],"long":[67],"periods.":[68],"To":[69],"address":[70],"these":[71],"problems,":[72],"this":[73],"paper":[74],"introduces":[75],"PSNet,":[76,170],"a":[77],"novel":[78],"model":[79,132],"designed":[80],"tackle":[82],"challenges":[84],"posed":[85],"complex":[87,183],"periodic":[88,162],"patterns":[89,126],"long-term":[91],"dependencies":[92],"real-world":[94],"data.":[97],"PSNet":[98],"integrates":[99],"into":[105],"framework,":[110],"enabling":[111],"decoupling":[113],"of":[114,121,146,160,169],"periods":[116],"effective":[119],"capture":[120],"both":[122],"high-frequency":[123],"low-frequency":[125],"series.":[129],"Specifically,":[130],"incorporates":[133],"two":[134],"key":[135],"components:":[136],"intra-period":[138],"module,":[141,155],"which":[142,156],"enhances":[143],"periodicity":[147],"within":[148],"individual":[149],"periods,":[150],"facilitates":[157],"fusion":[159],"diverse":[161],"patterns.":[163],"Extensive":[164],"experiments":[165],"demonstrate":[166],"superiority":[168],"achieving":[171],"state-of-the-art":[172],"performance":[173],"tasks":[176],"showcasing":[178],"its":[179],"ability":[180],"handle":[182],"temporal":[184],"dynamics":[185]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}