{"id":"https://openalex.org/W4408352592","doi":"https://doi.org/10.1109/icassp49660.2025.10887837","title":"Dilated Convolution for Time Series Learning","display_name":"Dilated Convolution for Time Series Learning","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408352592","doi":"https://doi.org/10.1109/icassp49660.2025.10887837"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10887837","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10887837","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100329318","display_name":"Wang Zhang","orcid":"https://orcid.org/0000-0002-8636-2816"},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wang Zhang","raw_affiliation_strings":["MIT,Cambridge,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT,Cambridge,MA,USA","institution_ids":["https://openalex.org/I4210110987"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011183160","display_name":"Subhro Das","orcid":"https://orcid.org/0000-0002-7610-2738"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subhro Das","raw_affiliation_strings":["MIT-IBM Watson AI Lab,IBM Research,Cambridge,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT-IBM Watson AI Lab,IBM Research,Cambridge,MA,USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049043092","display_name":"Lam M. Nguyen","orcid":"https://orcid.org/0000-0001-6083-606X"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lam M. Nguyen","raw_affiliation_strings":["Thomas J. Watson Research Center,IBM Research,Yorktown Heights,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Thomas J. Watson Research Center,IBM Research,Yorktown Heights,NY,USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021651870","display_name":"Luca Daniel","orcid":"https://orcid.org/0000-0002-5880-3151"},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luca Daniel","raw_affiliation_strings":["MIT,Cambridge,MA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT,Cambridge,MA,USA","institution_ids":["https://openalex.org/I4210110987"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9904000163078308,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9904000163078308,"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/T10320","display_name":"Neural Networks and Applications","score":0.958299994468689,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6819433569908142},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.668866753578186},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.64886474609375},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45617955923080444},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33486318588256836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3049996495246887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14957770705223083},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08465823531150818}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819433569908142},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.668866753578186},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.64886474609375},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45617955923080444},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33486318588256836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3049996495246887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14957770705223083},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08465823531150818},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10887837","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10887837","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1968354112","https://openalex.org/W1984674851","https://openalex.org/W2035104901","https://openalex.org/W2524083015","https://openalex.org/W2551393996","https://openalex.org/W2555077524","https://openalex.org/W2658572642","https://openalex.org/W2766799878","https://openalex.org/W2888791883","https://openalex.org/W2954112873","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W3134882343","https://openalex.org/W3160590016","https://openalex.org/W3163489177","https://openalex.org/W3177318507","https://openalex.org/W3188872815","https://openalex.org/W4382203079","https://openalex.org/W6698169118","https://openalex.org/W6755529311","https://openalex.org/W6797155008","https://openalex.org/W6810637551","https://openalex.org/W6840726259","https://openalex.org/W6845625448","https://openalex.org/W6889955440"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2053286651","https://openalex.org/W2181743346","https://openalex.org/W2187401768","https://openalex.org/W1919101720","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"The":[0,75],"state-of-the-art":[1,133],"(SOTA)":[2],"deep":[3],"learning":[4],"based":[5],"time":[6,38,64,106,121,162],"series":[7,39,65,107,122,163],"models":[8,134],"are":[9,23],"inspired":[10],"by":[11,140],"convolutional":[12,72,83],"neural":[13,17,52],"networks":[14,18,73],"(CNN),":[15],"recurrent":[16],"(RNN)":[19],"or":[20],"transformers":[21],"which":[22],"successful":[24],"architectures":[25],"for":[26,37,63],"domains":[27],"like":[28],"vision,":[29],"text,":[30],"etc.":[31],"However,":[32],"the":[33,79,101,111,114,128,138],"gold":[34],"standard":[35],"architecture":[36],"modeling":[40],"is":[41,86,97],"not":[42],"yet":[43],"established.":[44],"In":[45],"this":[46],"paper,":[47],"we":[48,148],"propose":[49],"a":[50,60,87,92,118,141],"new":[51],"network":[53,84],"structure":[54],"that":[55,90,127],"can":[56],"be":[57],"used":[58],"as":[59],"strong":[61],"baseline":[62,129],"problems,":[66],"leveraging":[67],"dilated":[68,80],"kernels":[69],"with":[70],"fully":[71,82],"(FCNs).":[74],"proposed":[76],"model,":[77],"called":[78],"multi-kernel":[81],"(DM-FCN),":[85],"composite":[88],"model":[89,116,131,158],"leverages":[91],"vast":[93],"receptive":[94],"field":[95],"and":[96,155],"designed":[98],"to":[99],"capture":[100],"long-distance":[102],"interaction":[103],"in":[104],"multivariate":[105],"data.":[108,164],"We":[109],"evaluate":[110],"performance":[112],"of":[113,120,137,153],"DM-FCN":[115,130,154],"on":[117,135,157],"variety":[119],"benchmarks.":[123],"Our":[124],"results":[125],"show":[126],"outperforms":[132],"many":[136],"benchmarks":[139],"large":[142],"margin.":[143],"By":[144],"integrating":[145],"statistical":[146],"insights,":[147],"also":[149],"evaluated":[150],"different":[151],"variations":[152],"deliberated":[156],"selections":[159],"across":[160],"diverse":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
