{"id":"https://openalex.org/W7126404571","doi":"https://doi.org/10.21428/594757db.7137edbd","title":"Interpreting time series forecasting models using model classreliance","display_name":"Interpreting time series forecasting models using model classreliance","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W7126404571","doi":"https://doi.org/10.21428/594757db.7137edbd"},"language":"en","primary_location":{"id":"doi:10.21428/594757db.7137edbd","doi":"https://doi.org/10.21428/594757db.7137edbd","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7137edbd","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.21428/594757db.7137edbd","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122025579","display_name":"Sean Berry","orcid":"https://orcid.org/0000-0002-6892-5865","observed_orcids":["https://orcid.org/0000-0002-6892-5865"]},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sean Berry","raw_affiliation_strings":["Toronto Metropolitan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093692461","display_name":"M\u00fccahit \u00c7evik","orcid":"https://orcid.org/0009-0000-0876-6287","observed_orcids":["https://orcid.org/0009-0000-0876-6287"]},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mucahit Cevik","raw_affiliation_strings":["Toronto Metropolitan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064988998","display_name":"Ozan Ozyegen","orcid":"https://orcid.org/0000-0002-7493-8056","observed_orcids":["https://orcid.org/0000-0002-7493-8056"]},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ozan Ozyegen","raw_affiliation_strings":["Toronto Metropolitan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toronto Metropolitan University","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I530967"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.0,"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":0.0,"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-forecasting","display_name":"time series forecasting","score":0.777999997138977},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.828000009059906},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7739999890327454},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"probabilistic forecasting","score":0.9639999866485596},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.9520000219345093},{"id":"https://openalex.org/keywords/post-hoc-explanation","display_name":"post-hoc explanation","score":0.7390000224113464},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.5199999809265137}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9768999814987183},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.73580002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6539000272750854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6486999988555908},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6244000196456909},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5957000255584717},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5584999918937683},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5303999781608582},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5260000228881836},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.384799987077713},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C82257358","wikidata":"https://www.wikidata.org/wiki/Q895497","display_name":"Box\u2013Jenkins","level":4,"score":0.2856000065803528}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21428/594757db.7137edbd","doi":"https://doi.org/10.21428/594757db.7137edbd","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7137edbd","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.21428/594757db.7137edbd","doi":"https://doi.org/10.21428/594757db.7137edbd","is_oa":true,"landing_page_url":"https://doi.org/10.21428/594757db.7137edbd","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Canadian Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4405173419","https://openalex.org/W3184473617","https://openalex.org/W4360832008","https://openalex.org/W3044448506","https://openalex.org/W3110835986","https://openalex.org/W4415835433","https://openalex.org/W4403724062","https://openalex.org/W7083806133","https://openalex.org/W3087024133","https://openalex.org/W4378471259"],"abstract_inverted_index":{"The":[0],"widespread":[1],"use":[2],"of":[3,79,175],"complex":[4,186],"machine":[5],"learning":[6],"models":[7,21,73,97],"in":[8,29,150],"various":[9],"data":[10],"science":[11],"tasks":[12],"has":[13],"made":[14],"it":[15,180],"difficult":[16],"to":[17,25,112],"understand":[18],"how":[19],"these":[20],"make":[22],"decisions,":[23],"leading":[24],"a":[26,181],"growing":[27],"interest":[28],"AI":[30],"interpretability.":[31],"This":[32,55],"is":[33],"especially":[34],"true":[35],"for":[36,69,81,141,172,184],"time":[37,70,83,94,187],"series":[38,71,84,95,188],"data,":[39],"where":[40],"applying":[41],"standard":[42],"interpretability":[43,67,90],"methods":[44],"introduces":[45],"unique":[46],"challenges":[47],"that":[48,132,158],"have":[49],"not":[50,161],"been":[51],"fully":[52],"explored":[53],"yet.":[54],"paper":[56],"compares":[57],"the":[58,76,104,118],"model":[59,127],"class":[60],"reliance":[61],"(MR)":[62],"scores":[63,102,134],"with":[64,137,164],"existing":[65],"state-of-the-art":[66],"measures":[68],"forecasting":[72,96,115,153,189],"and":[74,98,126,144,177],"provides":[75],"first":[77],"application":[78],"MR":[80,101,111,133,168],"probabilistic":[82,114,152],"forecasting.":[85],"We":[86],"employ":[87],"several":[88],"post-hoc":[89],"techniques":[91],"on":[92],"different":[93],"datasets,":[99],"comparing":[100],"against":[103,120],"benchmark":[105],"SHAP":[106,138,165],"scores.":[107],"Additionally,":[108],"we":[109],"apply":[110],"deep":[113,151],"models,":[116],"evaluating":[117],"outcomes":[119],"those":[121],"achieved":[122],"through":[123],"feature":[124,148],"removal":[125],"retraining.":[128],"Our":[129,155],"findings":[130],"indicate":[131],"are":[135],"consistent":[136],"scores,":[139],"particularly":[140],"important":[142],"features,":[143],"can":[145],"accurately":[146],"prioritize":[147],"importance":[149],"models.":[154,190],"work":[156],"demonstrates":[157],"MR-based":[159],"interpretations":[160],"only":[162],"align":[163],"but":[166],"also":[167],"method":[169],"stands":[170],"out":[171],"its":[173],"ease":[174],"implementation":[176],"efficiency,":[178],"making":[179],"viable":[182],"option":[183],"interpreting":[185]},"counts_by_year":[],"updated_date":"2026-11-10T13:44:29.811591","created_date":"2026-02-02T00:00:00"}