{"id":"https://openalex.org/W7134044295","doi":"https://doi.org/10.1007/s10994-026-07020-2","title":"AALF: Almost Always Linear Forecasting","display_name":"AALF: Almost Always Linear Forecasting","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7134044295","doi":"https://doi.org/10.1007/s10994-026-07020-2"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-026-07020-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-026-07020-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-026-07020-2.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/s10994-026-07020-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128248591","display_name":"Matthias Jakobs","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]},{"id":"https://openalex.org/I4387154616","display_name":"Lamarr Institute for Machine Learning and Artificial Intelligence","ror":"https://ror.org/04s11ea33","country_code":null,"type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I200332995","https://openalex.org/I4210098062","https://openalex.org/I4210144576","https://openalex.org/I4387154616","https://openalex.org/I4923324","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Matthias Jakobs","raw_affiliation_strings":["Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","institution_ids":["https://openalex.org/I4387154616"]},{"raw_affiliation_string":"TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128235786","display_name":"Thomas Liebig","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]},{"id":"https://openalex.org/I4387154616","display_name":"Lamarr Institute for Machine Learning and Artificial Intelligence","ror":"https://ror.org/04s11ea33","country_code":null,"type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I200332995","https://openalex.org/I4210098062","https://openalex.org/I4210144576","https://openalex.org/I4387154616","https://openalex.org/I4923324","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Liebig","raw_affiliation_strings":["Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany","institution_ids":["https://openalex.org/I4387154616"]},{"raw_affiliation_string":"TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5128248591"],"corresponding_institution_ids":["https://openalex.org/I200332995","https://openalex.org/I4387154616"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2147502,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"115","issue":"3","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":0.4717000126838684,"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":0.4717000126838684,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.2257000058889389,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.041200000792741776,"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/interpretability","display_name":"Interpretability","score":0.8228999972343445},{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"Exponential smoothing","score":0.7383000254631042},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.682699978351593},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.57669997215271},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5206000208854675},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.43130001425743103},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.3727000057697296}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8228999972343445},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.7383000254631042},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.682699978351593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6822999715805054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6783000230789185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6211000084877014},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.57669997215271},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.3727000057697296},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C161657586","wikidata":"https://www.wikidata.org/wiki/Q1203326","display_name":"Technology forecasting","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26089999079704285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10994-026-07020-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-026-07020-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-026-07020-2.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"}],"best_oa_location":{"id":"doi:10.1007/s10994-026-07020-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-026-07020-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-026-07020-2.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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7516871094703674}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321114","display_name":"Bundesministerium f\u00fcr Bildung und Forschung","ror":"https://ror.org/04pz7b180"},{"id":"https://openalex.org/F4320329559","display_name":"Technische Universit\u00e4t Dortmund","ror":"https://ror.org/01k97gp34"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7134044295.pdf","grobid_xml":"https://content.openalex.org/works/W7134044295.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2032927332","https://openalex.org/W2092126505","https://openalex.org/W2099419573","https://openalex.org/W2117014758","https://openalex.org/W2141408223","https://openalex.org/W2151554678","https://openalex.org/W2162174678","https://openalex.org/W2278984902","https://openalex.org/W2616247523","https://openalex.org/W2777265487","https://openalex.org/W2783209257","https://openalex.org/W2808800115","https://openalex.org/W2883806129","https://openalex.org/W2904765547","https://openalex.org/W2945976633","https://openalex.org/W2947014533","https://openalex.org/W2962752580","https://openalex.org/W2962772482","https://openalex.org/W2971973336","https://openalex.org/W2999615587","https://openalex.org/W3005102752","https://openalex.org/W3213767025","https://openalex.org/W4285036421","https://openalex.org/W4289524721","https://openalex.org/W4302438404","https://openalex.org/W4386811777","https://openalex.org/W4388409016","https://openalex.org/W4390957276","https://openalex.org/W4391549788","https://openalex.org/W4401353384","https://openalex.org/W4402115330","https://openalex.org/W4412876983","https://openalex.org/W7133184166"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Recent":[1],"work":[2],"for":[3,37,91,167,177],"time-series":[4,132,182],"forecasting":[5,47,79,101,183],"increasingly":[6],"leverages":[7],"the":[8,29,42,78,96,100,175],"high":[9],"predictive":[10],"power":[11],"of":[12,28,99],"Deep":[13,61,87],"Learning":[14,62,88],"models.":[15],"With":[16],"this":[17,104],"increase":[18],"in":[19,26,74,146,170,181],"model":[20,31,111,143,166],"complexity,":[21],"however,":[22],"comes":[23],"a":[24,86,108,160],"lack":[25],"understanding":[27],"underlying":[30],"decision":[32],"process,":[33],"which":[34,114],"is":[35],"problematic":[36],"safety-critical":[38],"application":[39],"scenarios.":[40],"At":[41],"same":[43],"time,":[44],"simple,":[45],"interpretable":[46,68],"methods":[48,145],"such":[49],"as":[50],"ARIMA":[51],"and":[52],"ETS":[53],"still":[54],"perform":[55],"very":[56],"well,":[57],"sometimes":[58],"on-par":[59],"with":[60],"approaches.":[63],"We":[64,154],"argue":[65],"that":[66,134,156,174],"using":[67],"forecasters":[69],"leads":[70],"to":[71,116,140],"good":[72],"predictions":[73],"most":[75,147],"cases.":[76],"However,":[77],"performance":[80],"can":[81],"be":[82,185],"improved":[83],"by":[84],"selecting":[85],"method":[89],"only":[90],"few,":[92],"important":[93],"predictions,":[94],"increasing":[95],"overall":[97],"interpretability":[98],"process.":[102],"In":[103],"context,":[105],"we":[106],"propose":[107],"novel":[109],"online":[110,142],"selection":[112,136,144],"framework":[113],"learns":[115],"identify":[117],"these":[118],"predictions.":[119],"An":[120],"extensive":[121],"empirical":[122],"study":[123],"on":[124],"various":[125],"real-world":[126],"datasets":[127],"containing":[128],"over":[129],"3500":[130],"individual":[131],"shows":[133],"our":[135],"methodology":[137],"performs":[138],"comparable":[139],"state-of-the-art":[141],"cases":[148],"while":[149],"being":[150],"significantly":[151],"more":[152],"interpretable.":[153],"find":[155],"almost":[157],"always":[158],"choosing":[159],"simple":[161],"autoregressive":[162],"or":[163],"exponential":[164],"smoothing":[165],"forecasting,":[168],"results":[169],"competitive":[171],"performance,":[172],"suggesting":[173],"need":[176],"opaque":[178],"black-box":[179],"models":[180],"might":[184],"smaller":[186],"than":[187],"recent":[188],"works":[189],"would":[190],"suggest.":[191]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-03-07T00:00:00"}