{"id":"https://openalex.org/W2014214435","doi":"https://doi.org/10.1145/1655925.1656102","title":"Contrast enhanced dynamic time warping distance for time series shape averaging classification","display_name":"Contrast enhanced dynamic time warping distance for time series shape averaging classification","publication_year":2009,"publication_date":"2009-11-24","ids":{"openalex":"https://openalex.org/W2014214435","doi":"https://doi.org/10.1145/1655925.1656102","mag":"2014214435"},"language":"en","primary_location":{"id":"doi:10.1145/1655925.1656102","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1655925.1656102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human","raw_type":"proceedings-article"},"type":"article","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/A5062578761","display_name":"Songpol Ongwattanakul","orcid":"https://orcid.org/0000-0002-7205-218X"},"institutions":[{"id":"https://openalex.org/I25399158","display_name":"Mahidol University","ror":"https://ror.org/01znkr924","country_code":"TH","type":"education","lineage":["https://openalex.org/I25399158"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Songpol Ongwattanakul","raw_affiliation_strings":["Mahidol University, Nakornpathom, Thailand"],"affiliations":[{"raw_affiliation_string":"Mahidol University, Nakornpathom, Thailand","institution_ids":["https://openalex.org/I25399158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021222023","display_name":"Dararat Srisai","orcid":null},"institutions":[{"id":"https://openalex.org/I158708052","display_name":"Chulalongkorn University","ror":"https://ror.org/028wp3y58","country_code":"TH","type":"education","lineage":["https://openalex.org/I158708052"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Dararat Srisai","raw_affiliation_strings":["Chulalongkorn University, Bangkok, Thailand","(Chulalongkorn University, Bangkok, Thailand)"],"affiliations":[{"raw_affiliation_string":"Chulalongkorn University, Bangkok, Thailand","institution_ids":["https://openalex.org/I158708052"]},{"raw_affiliation_string":"(Chulalongkorn University, Bangkok, Thailand)","institution_ids":["https://openalex.org/I158708052"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062578761"],"corresponding_institution_ids":["https://openalex.org/I25399158"],"apc_list":null,"apc_paid":null,"fwci":0.7049,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.66804221,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"28","issue":null,"first_page":"976","last_page":"981"},"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/T11309","display_name":"Music and Audio Processing","score":0.978600025177002,"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":0.9476000070571899,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.9831593632698059},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.738095760345459},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.7181708812713623},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7048379182815552},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6917135715484619},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.6676303148269653},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6584125757217407},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6259903907775879},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5977580547332764},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5616621375083923},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5446091294288635},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5150449275970459},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5038997530937195},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37485820055007935},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.23335179686546326},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09898295998573303},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07215940952301025}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.9831593632698059},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.738095760345459},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.7181708812713623},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7048379182815552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6917135715484619},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.6676303148269653},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6584125757217407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6259903907775879},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5977580547332764},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5616621375083923},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5446091294288635},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5150449275970459},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5038997530937195},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37485820055007935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.23335179686546326},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09898295998573303},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07215940952301025},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1655925.1656102","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1655925.1656102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W58346954","https://openalex.org/W69342273","https://openalex.org/W116902681","https://openalex.org/W1551040145","https://openalex.org/W1995003166","https://openalex.org/W2040944795","https://openalex.org/W2050722248","https://openalex.org/W2063411627","https://openalex.org/W2066834853","https://openalex.org/W2091921805","https://openalex.org/W2098759488","https://openalex.org/W2101005720","https://openalex.org/W2120194898","https://openalex.org/W2120440617","https://openalex.org/W2133184712","https://openalex.org/W2155919101","https://openalex.org/W4200084300"],"related_works":["https://openalex.org/W2513074791","https://openalex.org/W3111157199","https://openalex.org/W4281630436","https://openalex.org/W2014214435","https://openalex.org/W2182136398","https://openalex.org/W1982925424","https://openalex.org/W2902282441","https://openalex.org/W3141827490","https://openalex.org/W2052451333","https://openalex.org/W3111740253"],"abstract_inverted_index":{"Dynamic":[0,154],"Time":[1,19,155],"Warping":[2,156],"(DTW)":[3],"distance":[4,42,64,85,133],"has":[5],"been":[6,106],"a":[7,34,38,63,98,150],"focal":[8],"point":[9],"of":[10,176],"time":[11,53,77],"series":[12,20,78],"analysis":[13],"in":[14,66,130,171],"the":[15,45,49,71,74,82,91,116,124,131,144,160,169,177,183,188],"past":[16],"few":[17],"years.":[18],"data":[21,137,165],"mining":[22],"such":[23,114],"as":[24,62,115],"classification":[25,68,195],"and":[26,48,111,167,187],"clustering":[27],"normally":[28],"requires":[29],"shape":[30,79,192],"averaging":[31],"to":[32,36,143],"create":[33],"template":[35],"represent":[37],"class.":[39],"The":[40,55,174],"DTW":[41,56,84,103],"typically":[43],"provides":[44,73],"similarity":[46,57,88,172],"measure":[47,58,65,89],"alignment":[50,72],"between":[51],"two":[52],"series.":[54],"can":[59],"be":[60],"used":[61],"various":[67],"algorithms":[69],"while":[70],"basis":[75],"for":[76],"averaging.":[80],"Although":[81],"original":[83],"provide":[86],"better":[87],"than":[90],"Euclidean":[92],"distance,":[93],"some":[94,135],"accuracy":[95,110,125,170,175],"improvements":[96],"remain":[97],"challenge.":[99],"Recently,":[100],"many":[101],"novel":[102,151],"varieties":[104],"have":[105,140],"reported":[107],"with":[108],"greater":[109],"higher":[112],"performance":[113],"Resampled":[117],"DTW,":[118,120],"Hybrid":[119],"etc.":[121],"To":[122],"improve":[123],"further,":[126],"we":[127,148],"speculate":[128],"that,":[129],"DTW-based":[132],"measurements,":[134],"negligible":[136,164],"points":[138,166],"may":[139],"non-trivial":[141],"contribution":[142],"measured":[145],"distance.":[146],"Hence,":[147],"propose":[149],"Contrast":[152],"Enhanced":[153],"(CEDTW)":[157],"that":[158],"reduces":[159],"effect":[161],"from":[162],"those":[163],"improves":[168],"measure.":[173],"new":[178],"method":[179],"is":[180],"validated":[181],"against":[182],"classic":[184],"NLAAF,":[185],"PSA":[186],"latest":[189],"RSA":[190],"based":[191],"average":[193],"on":[194],"problems.":[196]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
