{"id":"https://openalex.org/W4412525554","doi":"https://doi.org/10.1007/s10618-025-01120-8","title":"Unpacking the trend: decomposition as a catalyst to enhance time series forecasting models","display_name":"Unpacking the trend: decomposition as a catalyst to enhance time series forecasting models","publication_year":2025,"publication_date":"2025-07-21","ids":{"openalex":"https://openalex.org/W4412525554","doi":"https://doi.org/10.1007/s10618-025-01120-8"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-025-01120-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01120-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01120-8.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","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/s10618-025-01120-8.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003274716","display_name":"Tim Kreuzer","orcid":"https://orcid.org/0000-0002-0813-9555"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Tim Kreuzer","raw_affiliation_strings":["Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008928633","display_name":"Jelena Zdravkovi\u0107","orcid":"https://orcid.org/0000-0002-0870-0330"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jelena Zdravkovic","raw_affiliation_strings":["Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044999523","display_name":"Panagiotis Papapetrou","orcid":"https://orcid.org/0000-0002-4632-4815"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Panagiotis Papapetrou","raw_affiliation_strings":["Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Systems Sciences, Stockholm University, Borgarfjordsgatan 12, 16455, Kista, Sweden","institution_ids":["https://openalex.org/I161593684"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003274716"],"corresponding_institution_ids":["https://openalex.org/I161593684"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":4.5699,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.94562307,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"39","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9986000061035156,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9986000061035156,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9958999752998352,"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":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/unpacking","display_name":"Unpacking","score":0.9845880270004272},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.6889725923538208},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6263759732246399},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5741000771522522},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5209082961082458},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3625580668449402},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3512895405292511},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23769241571426392},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20216357707977295}],"concepts":[{"id":"https://openalex.org/C2777256151","wikidata":"https://www.wikidata.org/wiki/Q7897273","display_name":"Unpacking","level":2,"score":0.9845880270004272},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.6889725923538208},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6263759732246399},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5741000771522522},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5209082961082458},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3625580668449402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3512895405292511},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23769241571426392},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20216357707977295},{"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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10618-025-01120-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01120-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01120-8.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10618-025-01120-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-025-01120-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-025-01120-8.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320325669","display_name":"Stockholms Universitet","ror":"https://ror.org/05f0yaq80"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412525554.pdf","grobid_xml":"https://content.openalex.org/works/W4412525554.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1975964493","https://openalex.org/W1988920820","https://openalex.org/W2007221293","https://openalex.org/W2024081693","https://openalex.org/W2061171222","https://openalex.org/W2064675550","https://openalex.org/W2100564287","https://openalex.org/W2132984323","https://openalex.org/W2177340570","https://openalex.org/W2604847698","https://openalex.org/W2747599906","https://openalex.org/W2808955427","https://openalex.org/W2900749811","https://openalex.org/W2947014533","https://openalex.org/W2963507686","https://openalex.org/W2964199361","https://openalex.org/W2980994438","https://openalex.org/W3093451924","https://openalex.org/W3115576324","https://openalex.org/W3177318507","https://openalex.org/W4292483811","https://openalex.org/W4311129667","https://openalex.org/W4382203079","https://openalex.org/W4388118948","https://openalex.org/W4392222741","https://openalex.org/W4399695543","https://openalex.org/W6600109629","https://openalex.org/W6600421821","https://openalex.org/W6600424091","https://openalex.org/W6732322125"],"related_works":["https://openalex.org/W2980032325","https://openalex.org/W2042188247","https://openalex.org/W2017714611","https://openalex.org/W4224995949","https://openalex.org/W1992482086","https://openalex.org/W2385797406","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Abstract":[0],"For":[1],"the":[2,17,40,46,62,112,120],"time":[3,24,161],"series":[4,25,162],"forecasting":[5,26,47,57,71,87,108,125,163],"task,":[6,48],"several":[7],"state-of-the-art":[8],"algorithms":[9],"employ":[10],"moving-average":[11],"decomposition":[12,20,43,51,78,98,105,133,139,158],"for":[13,45,99,111],"improved":[14,129],"accuracy.":[15],"However,":[16],"potential":[18],"of":[19,42,64,123,148],"techniques":[21,52,140],"to":[22,73,150],"enhance":[23,160],"methods":[27,44],"has":[28],"not":[29],"been":[30],"explored":[31],"in":[32,85],"detail.":[33],"In":[34],"this":[35],"work,":[36],"we":[37,117],"comprehensively":[38],"investigate":[39],"use":[41],"comparing":[49],"different":[50,86,132],"and":[53,80,92,169],"their":[54,83,167],"effect":[55],"on":[56],"accuracy,":[58,109],"as":[59,61],"well":[60],"possibility":[63],"providing":[65],"model-agnostic":[66,95,152],"interpretability.":[67,100,153,170],"We":[68,89,136],"rework":[69],"recent":[70],"models":[72,126],"be":[74,128,142],"compatible":[75],"with":[76,144],"any":[77],"technique":[79],"experimentally":[81],"evaluate":[82],"effectiveness":[84],"setups.":[88],"further":[90],"propose":[91],"assess":[93],"a":[94,145],"framework":[96],"using":[97,131],"Our":[101,154],"results":[102],"show":[103],"that":[104,119,138,157],"can":[106,127,159],"improve":[107],"especially":[110],"proposed":[113],"decomposition-adapted":[114],"models.":[115],"Additionally,":[116],"demonstrate":[118],"architectural":[121],"choices":[122],"existing":[124],"by":[130],"blocks":[134],"internally.":[135],"found":[137],"must":[141],"configured":[143],"low":[146],"number":[147],"components":[149],"provide":[151],"work":[155],"concludes":[156],"algorithms,":[164],"improving":[165],"both":[166],"performance":[168]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
