{"id":"https://openalex.org/W2222021316","doi":"https://doi.org/10.1109/fskd.2015.7382086","title":"Empirical studies on Symbolic Aggregation approximation under statistical perspectives for knowledge discovery in time series","display_name":"Empirical studies on Symbolic Aggregation approximation under statistical perspectives for knowledge discovery in time series","publication_year":2015,"publication_date":"2015-08-01","ids":{"openalex":"https://openalex.org/W2222021316","doi":"https://doi.org/10.1109/fskd.2015.7382086","mag":"2222021316"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2015.7382086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2015.7382086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","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/A5100628749","display_name":"Wei Song","orcid":"https://orcid.org/0000-0001-6623-4051"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Song","raw_affiliation_strings":["School of Information Engineering, Zhengzhou University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100637055","display_name":"Zhiguang Wang","orcid":"https://orcid.org/0000-0001-7045-5308"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiguang Wang","raw_affiliation_strings":["Dept. of CS and EE, University of Maryland, Baltimore, Maryland, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of CS and EE, University of Maryland, Baltimore, Maryland, USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045940041","display_name":"Yangdong Ye","orcid":"https://orcid.org/0000-0001-7027-8313"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangdong Ye","raw_affiliation_strings":["School of Information Engineering, Zhengzhou University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100692958","display_name":"Ming Fan","orcid":"https://orcid.org/0000-0002-9327-0987"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Fan","raw_affiliation_strings":["School of Information Engineering, Zhengzhou University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Zhengzhou University, China","institution_ids":["https://openalex.org/I38877650"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100628749"],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":0.2872,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57520989,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"1040","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9975000023841858,"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":0.9975000023841858,"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":0.983299970626831,"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/T10320","display_name":"Neural Networks and Applications","score":0.9736999869346619,"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/series","display_name":"Series (stratigraphy)","score":0.6950583457946777},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682420969009399},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.552948534488678},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.5185533165931702},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4883684515953064},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.4397684931755066},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35814017057418823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3553887605667114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30979809165000916},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17485728859901428},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16545924544334412}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6950583457946777},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682420969009399},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.552948534488678},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.5185533165931702},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4883684515953064},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4397684931755066},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35814017057418823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3553887605667114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30979809165000916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17485728859901428},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16545924544334412},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/fskd.2015.7382086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2015.7382086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)","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":31,"referenced_works":["https://openalex.org/W1259078","https://openalex.org/W1499049447","https://openalex.org/W1559781006","https://openalex.org/W1965555277","https://openalex.org/W1966406756","https://openalex.org/W1989037929","https://openalex.org/W1993220186","https://openalex.org/W2002213744","https://openalex.org/W2014644667","https://openalex.org/W2014683958","https://openalex.org/W2026909728","https://openalex.org/W2034570973","https://openalex.org/W2055333804","https://openalex.org/W2066796814","https://openalex.org/W2081028405","https://openalex.org/W2084481683","https://openalex.org/W2089136469","https://openalex.org/W2103769069","https://openalex.org/W2105510466","https://openalex.org/W2124459709","https://openalex.org/W2127719586","https://openalex.org/W2148039410","https://openalex.org/W2171029115","https://openalex.org/W2318640187","https://openalex.org/W2539912446","https://openalex.org/W2734451155","https://openalex.org/W4237171445","https://openalex.org/W4242702158","https://openalex.org/W4247160275","https://openalex.org/W4254407586","https://openalex.org/W6728799417"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W3046775127","https://openalex.org/W4309045103","https://openalex.org/W4213225422","https://openalex.org/W3164310557","https://openalex.org/W1830619230"],"abstract_inverted_index":{"Symbolic":[0],"Aggregation":[1],"approXimation":[2],"(SAX)":[3],"has":[4,30],"been":[5,31],"the":[6,36,66,70,76,80,89,93,98,147],"de":[7],"facto":[8],"standard":[9],"representation":[10],"methods":[11],"for":[12,119,150],"knowledge":[13,151],"discovery":[14,152],"in":[15,33,42,97,153],"time":[16,100,154],"series":[17,101],"on":[18,75],"a":[19,55,111],"number":[20],"of":[21,69],"tasks":[22],"and":[23,39,53,79,144],"applications.":[24],"So":[25],"far,":[26],"very":[27],"little":[28],"work":[29,132],"done":[32],"empirically":[34],"investigating":[35],"intrinsic":[37],"properties":[38],"statistical":[40,51,57,67,139],"mechanics":[41],"SAX":[43,85,116],"words.":[44],"In":[45],"this":[46],"paper,":[47],"we":[48],"applied":[49],"several":[50,138],"measurements":[52],"proposed":[54,107],"new":[56],"measurement,":[58],"i.e.":[59],"information":[60,95],"embedding":[61,104],"cost":[62],"(IEC)":[63],"to":[64,113,126,141],"analyze":[65],"behaviors":[68],"symbolic":[71,129,148],"dynamics.":[72],"Our":[73,106,131],"experiments":[74],"benchmark":[77],"datasets":[78],"clinical":[81],"signals":[82],"demonstrate":[83],"that":[84],"can":[86,123],"always":[87],"reduce":[88],"complexity":[90],"while":[91],"preserving":[92],"core":[94],"embedded":[96],"original":[99],"with":[102,137],"significant":[103],"efficiency.":[105],"IEC":[108],"score":[109],"provide":[110],"priori":[112],"determine":[114],"if":[115],"is":[117],"adequate":[118],"specific":[120],"dataset,":[121],"which":[122],"be":[124],"generalized":[125],"evaluate":[127,143],"other":[128],"representations.":[130],"provides":[133],"an":[134],"analytical":[135],"framework":[136],"tools":[140],"analyze,":[142],"further":[145],"improve":[146],"dynamics":[149],"series.":[155]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}