{"id":"https://openalex.org/W4414154053","doi":"https://doi.org/10.1007/s10994-025-06877-z","title":"Modelradar: aspect-based forecast evaluation","display_name":"Modelradar: aspect-based forecast evaluation","publication_year":2025,"publication_date":"2025-09-09","ids":{"openalex":"https://openalex.org/W4414154053","doi":"https://doi.org/10.1007/s10994-025-06877-z"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-025-06877-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06877-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06877-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","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/s10994-025-06877-z.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018859851","display_name":"V\u00edtor Cerqueira","orcid":"https://orcid.org/0000-0002-9694-8423"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Vitor Cerqueira","raw_affiliation_strings":["Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]},{"raw_affiliation_string":"Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075614965","display_name":"Lu\u00eds Roque","orcid":"https://orcid.org/0000-0002-0899-3209"},"institutions":[{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Luis Roque","raw_affiliation_strings":["Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]},{"raw_affiliation_string":"Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058183984","display_name":"Carlos Soares","orcid":"https://orcid.org/0000-0003-4549-8917"},"institutions":[{"id":"https://openalex.org/I4210157425","display_name":"Fraunhofer Portugal Research","ror":"https://ror.org/05eqk2j25","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210157425","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I182534213","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612","country_code":"PT","type":"education","lineage":["https://openalex.org/I182534213"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Carlos Soares","raw_affiliation_strings":["Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","Fraunhofer Portugal AICOS, Porto, Portugal","Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal"],"affiliations":[{"raw_affiliation_string":"Faculdade de Engenharia, Universidade do Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]},{"raw_affiliation_string":"Fraunhofer Portugal AICOS, Porto, Portugal","institution_ids":["https://openalex.org/I4210157425"]},{"raw_affiliation_string":"Laboratory for Artificial Intelligence and Computer Science (LIACC), University of Porto, Porto, Portugal","institution_ids":["https://openalex.org/I182534213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018859851"],"corresponding_institution_ids":["https://openalex.org/I182534213"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28383775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"114","issue":"10","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":0.9991000294685364,"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":0.9991000294685364,"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":0.9975000023841858,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.699999988079071},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6425999999046326},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5514000058174133},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49050000309944153},{"id":"https://openalex.org/keywords/technology-forecasting","display_name":"Technology forecasting","score":0.39969998598098755},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.39719998836517334}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7057999968528748},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.699999988079071},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6425999999046326},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5922999978065491},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5514000058174133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5419999957084656},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49050000309944153},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3901999890804291},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.2662000060081482}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-025-06877-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06877-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06877-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:zenodo.org:17341254","is_oa":true,"landing_page_url":"https://arxiv.org/abs/arXiv:2504.00059","pdf_url":"https://arxiv.org/pdf/2504.00059","source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning, 114(229), (2025-03-31)","raw_type":"info:eu-repo/semantics/other"}],"best_oa_location":{"id":"doi:10.1007/s10994-025-06877-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06877-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06877-z.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"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":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324086","display_name":"Universidade do Porto","ror":"https://ror.org/043pwc612"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414154053.pdf","grobid_xml":"https://content.openalex.org/works/W4414154053.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1547333707","https://openalex.org/W1588163064","https://openalex.org/W1977698057","https://openalex.org/W1982348007","https://openalex.org/W1989280111","https://openalex.org/W2040395995","https://openalex.org/W2048665112","https://openalex.org/W2053865013","https://openalex.org/W2116512828","https://openalex.org/W2132782512","https://openalex.org/W2146525523","https://openalex.org/W2154326182","https://openalex.org/W2280712870","https://openalex.org/W2794778778","https://openalex.org/W2801776818","https://openalex.org/W2808800115","https://openalex.org/W2962752580","https://openalex.org/W2966607134","https://openalex.org/W2980994438","https://openalex.org/W3005448318","https://openalex.org/W3130453397","https://openalex.org/W3155052204","https://openalex.org/W3171884590","https://openalex.org/W3177318507","https://openalex.org/W4206173445","https://openalex.org/W4230410911","https://openalex.org/W4280502440","https://openalex.org/W4311129667","https://openalex.org/W4353081850","https://openalex.org/W4382239356","https://openalex.org/W4406844761","https://openalex.org/W4409363931"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Abstract":[0],"Accurate":[1],"evaluation":[2,196],"of":[3,61,100,109,184,193],"forecasting":[4,18,91,103,115,140,146],"models":[5,19,92],"is":[6,51,168,203],"essential":[7],"for":[8,14,54,86,161,197],"ensuring":[9],"reliable":[10],"predictions.":[11],"Current":[12],"practices":[13],"evaluating":[15,87],"and":[16,66,120,187,200],"comparing":[17,113],"focus":[20],"on":[21],"summarising":[22],"performance":[23,36,75],"into":[24],"a":[25,71,84,126,206],"single":[26],"score,":[27],"using":[28],"metrics":[29],"such":[30,96,172],"as":[31,97,173,205],"SMAPE.":[32],"While":[33],"convenient,":[34],"averaging":[35],"over":[37],"all":[38],"samples":[39],"dilutes":[40],"relevant":[41,74,166],"information":[42],"about":[43],"model":[44,195],"behaviour":[45],"under":[46],"varying":[47],"conditions.":[48,141],"This":[49],"limitation":[50,80],"especially":[52],"problematic":[53],"time":[55,63,68,89],"series":[56,69,90],"forecasting,":[57],"where":[58],"multiple":[59,67,94],"layers":[60],"averaging\u2013across":[62],"steps,":[64],"horizons,":[65],"in":[70,181],"dataset\u2013can":[72],"mask":[73],"variations.":[76],"We":[77,105],"address":[78],"this":[79,110],"by":[81,112],"proposing":[82],"ModelRadar,":[83],"framework":[85,111],"univariate":[88],"across":[93],"aspects,":[95],"stationarity,":[98],"presence":[99,183],"anomalies,":[101],"or":[102,175],"horizons.":[104],"demonstrate":[106],"the":[107,145,182,191],"advantages":[108],"24":[114],"methods,":[116],"including":[117],"classical":[118,159,170],"approaches":[119,160,171],"different":[121],"machine":[122],"learning":[123],"algorithms.":[124],"PatchTST,":[125],"state-of-the-art":[127],"transformer-based":[128],"neural":[129,155],"network":[130],"architecture,":[131],"performs":[132],"best":[133],"overall":[134],"but":[135],"its":[136],"superiority":[137],"varies":[138],"with":[139],"For":[142],"instance,":[143],"concerning":[144],"horizon,":[147],"we":[148],"found":[149],"that":[150,169],"PatchTST":[151],"(and":[152],"also":[153],"other":[154,188],"networks)":[156],"only":[157],"outperforms":[158],"multi-step":[162],"ahead":[163],"forecasting.":[164],"Another":[165],"insight":[167],"ETS":[174],"Theta":[176],"are":[177],"notably":[178],"more":[179],"robust":[180],"anomalies.":[185],"These":[186],"findings":[189],"highlight":[190],"importance":[192],"aspect-based":[194],"both":[198],"practitioners":[199],"researchers.":[201],"ModelRadar":[202],"available":[204],"Python":[207],"package.":[208]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}