{"id":"https://openalex.org/W7134906733","doi":"https://doi.org/10.1109/icdmw69685.2025.00425","title":"Learning for Inflation Forecasting with Dynamic Feature Spaces","display_name":"Learning for Inflation Forecasting with Dynamic Feature Spaces","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134906733","doi":"https://doi.org/10.1109/icdmw69685.2025.00425"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5128745655","display_name":"Zakariyya Scavotto","orcid":null},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zakariyya Scavotto","raw_affiliation_strings":["Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127446028","display_name":"Xiaoxue Han","orcid":null},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoxue Han","raw_affiliation_strings":["Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA","institution_ids":["https://openalex.org/I108468826"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126995445","display_name":"Yue Ning","orcid":null},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Ning","raw_affiliation_strings":["Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology,Department of Computer Science,Hoboken,NJ,USA","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73730845,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3176","last_page":"3181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5665000081062317,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.5665000081062317,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.08820000290870667,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.06069999933242798,"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/feature","display_name":"Feature (linguistics)","score":0.5546000003814697},{"id":"https://openalex.org/keywords/inflation","display_name":"Inflation (cosmology)","score":0.3935999870300293},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.2913999855518341},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.27709999680519104},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.2705000042915344}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5546000003814697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4869999885559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39890000224113464},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3968000113964081},{"id":"https://openalex.org/C200941418","wikidata":"https://www.wikidata.org/wiki/Q273508","display_name":"Inflation (cosmology)","level":2,"score":0.3935999870300293},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.30959999561309814},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28220000863075256},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.47412124276161194,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G1567138803","display_name":"CAREER: Towards Deep Interpretable Predictions for Multi-Scope Temporal Events","funder_award_id":"2047843","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2565952817","https://openalex.org/W3010538321","https://openalex.org/W3122533884","https://openalex.org/W3123591922","https://openalex.org/W3147702120","https://openalex.org/W3196036470","https://openalex.org/W3216698414","https://openalex.org/W4206205685","https://openalex.org/W4283693860","https://openalex.org/W4285794683","https://openalex.org/W4323924507","https://openalex.org/W4362671915","https://openalex.org/W4391582546"],"related_works":[],"abstract_inverted_index":{"Inflation":[0],"forecasting":[1],"becomes":[2],"especially":[3],"challenging":[4],"during":[5,97],"structural":[6],"breaks,":[7],"such":[8],"as":[9],"the":[10,64],"COVID-19":[11],"pandemic,":[12],"when":[13],"historical":[14],"patterns":[15],"are":[16],"disrupted.":[17],"We":[18,58],"evaluate":[19],"lightweight":[20],"ML":[21],"strategies":[22],"for":[23],"adapting":[24],"to":[25],"dynamic":[26],"feature":[27],"spaces,":[28],"including":[29],"rolling-window":[30],"training,":[31],"interpolation,":[32],"and":[33,40,54],"reversible":[34],"normalization.":[35],"Using":[36],"U.S.":[37],"CPI":[38],"data":[39],"pandemic-specific":[41],"features":[42],"(cases,":[43],"deaths,":[44],"vaccines,":[45],"hospitalizations),":[46],"we":[47],"compare":[48],"traditional":[49],"econometric":[50],"models,":[51],"tree":[52],"ensembles,":[53],"deep":[55],"sequential":[56],"architectures.":[57],"find":[59],"that":[60,89],"ensemble":[61],"methods":[62,93],"deliver":[63],"most":[65],"accurate":[66],"forecasts":[67],"(RMSE":[68],"0.36":[69],"with":[70],"interpolated":[71],"training":[72],"data,":[73],"0.61":[74],"without),":[75],"while":[76],"normalization":[77],"layers":[78],"improve":[79],"neural":[80],"networks'":[81],"robustness":[82],"under":[83],"distribution":[84],"shifts.":[85],"These":[86],"results":[87],"show":[88],"simple,":[90],"low-cost":[91],"adaptation":[92],"sustain":[94],"forecast":[95],"reliability":[96],"shocks,":[98],"aiding":[99],"policymakers.":[100]},"counts_by_year":[],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2026-03-12T00:00:00"}