{"id":"https://openalex.org/W4386465411","doi":"https://doi.org/10.1007/s10618-023-00948-2","title":"Improving position encoding of transformers for multivariate time series classification","display_name":"Improving position encoding of transformers for multivariate time series classification","publication_year":2023,"publication_date":"2023-09-05","ids":{"openalex":"https://openalex.org/W4386465411","doi":"https://doi.org/10.1007/s10618-023-00948-2"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-023-00948-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-023-00948-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-023-00948-2.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","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/s10618-023-00948-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067175463","display_name":"Navid Mohammadi Foumani","orcid":"https://orcid.org/0000-0003-2475-6040"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Navid Mohammadi Foumani","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0003-2475-6040","affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088038766","display_name":"Chang Wei Tan","orcid":"https://orcid.org/0000-0001-8377-3241"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chang Wei Tan","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058054791","display_name":"Geoffrey I. Webb","orcid":"https://orcid.org/0000-0001-9963-5169"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Geoffrey I. Webb","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019440770","display_name":"Mahsa Salehi","orcid":"https://orcid.org/0000-0002-2991-1612"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mahsa Salehi","raw_affiliation_strings":["Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067175463"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":36.6333,"has_fulltext":true,"cited_by_count":200,"citation_normalized_percentile":{"value":0.99907149,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"38","issue":"1","first_page":"22","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.992900013923645,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6810333728790283},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.6168136596679688},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.609588623046875},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5147755146026611},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5121347904205322},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.49807214736938477},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4868704378604889},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42378729581832886},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37740713357925415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.369962602853775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19521218538284302},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07770678400993347}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6810333728790283},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.6168136596679688},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.609588623046875},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5147755146026611},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5121347904205322},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.49807214736938477},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4868704378604889},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42378729581832886},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37740713357925415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.369962602853775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19521218538284302},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07770678400993347},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10618-023-00948-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-023-00948-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-023-00948-2.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:monash.edu:publications/f22eb024-93bb-435d-b215-cadd5f1e845b","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85169842160&partnerID=8YFLogxK","pdf_url":"https://researchmgt.monash.edu/ws/files/580333001/516577306_oa.pdf","source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Foumani, N M, Tan, C W, Webb, G I & Salehi, M 2024, 'Improving position encoding of transformers for multivariate time series classification', Data Mining and Knowledge Discovery, vol. 38, pp. 22-48. https://doi.org/10.1007/s10618-023-00948-2","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:monash.edu:openaire/f22eb024-93bb-435d-b215-cadd5f1e845b","is_oa":true,"landing_page_url":"https://research.monash.edu/en/publications/f22eb024-93bb-435d-b215-cadd5f1e845b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402625","display_name":"Monash University Research Portal (Monash University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I56590836","host_organization_name":"Monash University","host_organization_lineage":["https://openalex.org/I56590836"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Foumani, N M, Tan, C W, Webb, G I & Salehi, M 2024, 'Improving position encoding of transformers for multivariate time series classification', Data Mining and Knowledge Discovery, vol. 38, pp. 22-48. https://doi.org/10.1007/s10618-023-00948-2","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10618-023-00948-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-023-00948-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-023-00948-2.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"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":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320971","display_name":"Monash University","ror":"https://ror.org/02bfwt286"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386465411.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1902237438","https://openalex.org/W2090805767","https://openalex.org/W2551393996","https://openalex.org/W2555077524","https://openalex.org/W2783323081","https://openalex.org/W2892035503","https://openalex.org/W2963163009","https://openalex.org/W2963925437","https://openalex.org/W2972810968","https://openalex.org/W2982438846","https://openalex.org/W3034999176","https://openalex.org/W3042807565","https://openalex.org/W3080921724","https://openalex.org/W3083891030","https://openalex.org/W3106210592","https://openalex.org/W3110709724","https://openalex.org/W3112330479","https://openalex.org/W3115948762","https://openalex.org/W3132607382","https://openalex.org/W3177342940","https://openalex.org/W3186145246","https://openalex.org/W3188872815","https://openalex.org/W3190461479","https://openalex.org/W3203701986","https://openalex.org/W3204643324","https://openalex.org/W4205114161","https://openalex.org/W6603139312","https://openalex.org/W6702248584"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2037549926","https://openalex.org/W2345479200","https://openalex.org/W2183306018","https://openalex.org/W2849310602","https://openalex.org/W3006008237","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Abstract":[0],"Transformers":[1],"have":[2],"demonstrated":[3],"outstanding":[4],"performance":[5],"in":[6,37,83,118],"many":[7],"applications":[8],"of":[9,27,34,64,128,164],"deep":[10],"learning.":[11],"When":[12],"applied":[13,82],"to":[14,23,52,68,97,133,157],"time":[15,29,38,84,98,102,137,145,165],"series":[16,30,39,85,99,112,146,166],"data,":[17],"transformers":[18],"require":[19],"effective":[20],"position":[21,35,55,59,78,93,120,160,173],"encoding":[22,36,56,79,94,154,174],"capture":[24],"the":[25,28,111,159],"ordering":[26],"data.":[31,167],"The":[32,168],"efficacy":[33],"analysis":[40],"is":[41,50,212],"not":[42],"well-studied":[43],"and":[44,76,114,151,161,171,178,188,198,219,223],"remains":[45],"controversial,":[46],"e.g.,":[47],"whether":[48],"it":[49],"better":[51],"inject":[53],"absolute":[54,75,92,119,170],"or":[57,61],"relative":[58,77,172],"encoding,":[60],"a":[62,90,142],"combination":[63],"them.":[65],"In":[66],"order":[67],"clarify":[69],"this,":[70],"we":[71,123],"first":[72],"review":[73],"existing":[74],"methods":[80,175],"when":[81],"classification.":[86],"We":[87,139],"then":[88,140],"proposed":[89,169],"new":[91,108],"method":[95,109],"dedicated":[96],"data":[100,162],"called":[101],"Absolute":[103],"Position":[104,130],"Encoding":[105,131],"(tAPE).":[106],"Our":[107],"incorporates":[110],"length":[113],"input":[115,153],"embedding":[116,163],"dimension":[117],"encoding.":[121],"Additionally,":[122],"propose":[124,141],"computationally":[125],"Efficient":[126],"implementation":[127],"Relative":[129],"(eRPE)":[132],"improve":[134,158],"generalisability":[135],"for":[136,190],"series.":[138],"novel":[143],"multivariate":[144,205],"classification":[147],"model":[148,211],"combining":[149],"tAPE/eRPE":[150],"convolution-based":[152],"named":[155],"ConvTran":[156],"are":[176,225],"simple":[177],"efficient.":[179],"They":[180],"can":[181],"be":[182],"easily":[183],"integrated":[184],"into":[185],"transformer":[186],"blocks":[187],"used":[189],"downstream":[191],"tasks":[192],"such":[193],"as":[194],"forecasting,":[195],"extrinsic":[196],"regression,":[197],"anomaly":[199],"detection.":[200],"Extensive":[201],"experiments":[202],"on":[203],"32":[204],"time-series":[206],"datasets":[207],"show":[208],"that":[209],"our":[210],"significantly":[213],"more":[214],"accurate":[215],"than":[216],"state-of-the-art":[217],"convolution":[218],"transformer-based":[220],"models.":[221],"Code":[222],"models":[224],"open-sourced":[226],"at":[227],"https://github.com/Navidfoumani/ConvTran":[228],".":[229]},"counts_by_year":[{"year":2026,"cited_by_count":49},{"year":2025,"cited_by_count":105},{"year":2024,"cited_by_count":42},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-21T07:57:09.225873","created_date":"2025-10-10T00:00:00"}
