{"id":"https://openalex.org/W2122504858","doi":"https://doi.org/10.1109/cidm.2013.6597219","title":"How to extract meaningful shapes from noisy time-series subsequences?","display_name":"How to extract meaningful shapes from noisy time-series subsequences?","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2122504858","doi":"https://doi.org/10.1109/cidm.2013.6597219","mag":"2122504858"},"language":"en","primary_location":{"id":"doi:10.1109/cidm.2013.6597219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","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/A5038747906","display_name":"Yanfei Kang","orcid":"https://orcid.org/0000-0001-8769-6650"},"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":"Yanfei Kang","raw_affiliation_strings":["School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039491324","display_name":"Kate Smith\u2010Miles","orcid":"https://orcid.org/0000-0003-2718-7680"},"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":"Kate Smith-Miles","raw_affiliation_strings":["School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036426553","display_name":"Danijel Belu\u0161i\u0107","orcid":"https://orcid.org/0000-0002-5665-3866"},"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":"Danijel Belusic","raw_affiliation_strings":["School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia"],"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Monash University, Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]},{"raw_affiliation_string":"Sch. of Math. Sci., Monash Univ., Clayton, VIC, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5038747906"],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":0.6384,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70370153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"117","issue":null,"first_page":"65","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998000264167786,"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":0.9998000264167786,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9950000047683716,"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"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9014000296592712,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/subsequence","display_name":"Subsequence","score":0.8085676431655884},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7509077191352844},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.741787314414978},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6577088832855225},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6218793392181396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5952908396720886},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5869580507278442},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47807955741882324},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4605153799057007},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.424361914396286},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.4229884743690491},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.42188698053359985},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4145389199256897},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40798306465148926},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33616548776626587},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.19017335772514343},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.17811334133148193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13126802444458008},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07359948754310608}],"concepts":[{"id":"https://openalex.org/C137877099","wikidata":"https://www.wikidata.org/wiki/Q1332977","display_name":"Subsequence","level":3,"score":0.8085676431655884},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7509077191352844},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.741787314414978},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6577088832855225},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6218793392181396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5952908396720886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5869580507278442},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47807955741882324},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4605153799057007},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.424361914396286},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.4229884743690491},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.42188698053359985},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4145389199256897},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40798306465148926},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33616548776626587},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.19017335772514343},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.17811334133148193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13126802444458008},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07359948754310608},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cidm.2013.6597219","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cidm.2013.6597219","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W151863654","https://openalex.org/W576081672","https://openalex.org/W1499049447","https://openalex.org/W1513731586","https://openalex.org/W1524855981","https://openalex.org/W1537039776","https://openalex.org/W1565809441","https://openalex.org/W1578920089","https://openalex.org/W1595324333","https://openalex.org/W1976361167","https://openalex.org/W1989280111","https://openalex.org/W2006761268","https://openalex.org/W2006783944","https://openalex.org/W2016287239","https://openalex.org/W2028903194","https://openalex.org/W2031271712","https://openalex.org/W2042591571","https://openalex.org/W2066796814","https://openalex.org/W2077592719","https://openalex.org/W2097997754","https://openalex.org/W2110704543","https://openalex.org/W2128528271","https://openalex.org/W2152893838","https://openalex.org/W2157009395","https://openalex.org/W2157473900","https://openalex.org/W2163336863","https://openalex.org/W2489540272","https://openalex.org/W3010932768","https://openalex.org/W3169372665","https://openalex.org/W6606194391","https://openalex.org/W6631405836","https://openalex.org/W6635575044","https://openalex.org/W6661122342","https://openalex.org/W6682603391"],"related_works":["https://openalex.org/W2073156614","https://openalex.org/W4230102134","https://openalex.org/W2996768723","https://openalex.org/W4212895949","https://openalex.org/W2112772040","https://openalex.org/W3216774880","https://openalex.org/W2078387789","https://openalex.org/W2100867659","https://openalex.org/W1581358077","https://openalex.org/W1582097743"],"abstract_inverted_index":{"A":[0],"method":[1,14,91,135],"for":[2,95,153],"extracting":[3,96],"and":[4,50,83,97,116,120,141,147],"classifying":[5,98],"shapes":[6,52,74,99,143,155],"from":[7,32,47,55,71,100,144],"noisy":[8,102,158],"time":[9,80,104,160],"series":[10,34,81],"is":[11,22,63,92,136],"proposed.":[12],"The":[13,19,60,90],"consists":[15],"of":[16,79,113],"two":[17],"steps.":[18],"first":[20],"step":[21,62],"to":[23,64,87,138],"perform":[24],"a":[25,36,76],"noise":[26,44,115,131],"test":[27],"on":[28],"each":[29],"subsequence":[30],"extracted":[31,54,67,70],"the":[33,40,51,56,117,121,134],"using":[35,107],"sliding":[37],"window.":[38],"All":[39],"subsequences":[41,82],"recognised":[42],"as":[43],"are":[45,53,84],"removed":[46],"further":[48],"analysis,":[49],"remaining":[57],"non-noise":[58],"subsequences.":[59],"second":[61],"cluster":[65,142],"these":[66,73],"shapes.":[68],"Although":[69],"subsequences,":[72],"form":[75],"non-overlapping":[77],"set":[78],"hence":[85],"amenable":[86],"meaningful":[88],"clustering.":[89],"primarily":[93],"designed":[94],"very":[101,157],"real-world":[103,122,159],"series.":[105,161],"Tests":[106],"artificial":[108,145],"data":[109,125,146],"with":[110],"different":[111],"levels":[112],"white":[114],"red":[118,130],"noise,":[119],"atmospheric":[123],"turbulence":[124],"naturally":[126],"characterised":[127],"by":[128],"strong":[129],"show":[132],"that":[133,148],"able":[137],"correctly":[139],"extract":[140],"it":[149],"has":[150],"great":[151],"potential":[152],"locating":[154],"in":[156]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}