{"id":"https://openalex.org/W2606410554","doi":"https://doi.org/10.1504/ijbidm.2017.10004687","title":"Pattern Matching-based Prediction using Affine Combination of Two Measures: Two are Better Than One","display_name":"Pattern Matching-based Prediction using Affine Combination of Two Measures: Two are Better Than One","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2606410554","doi":"https://doi.org/10.1504/ijbidm.2017.10004687","mag":"2606410554"},"language":"en","primary_location":{"id":"doi:10.1504/ijbidm.2017.10004687","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijbidm.2017.10004687","pdf_url":null,"source":{"id":"https://openalex.org/S47982532","display_name":"International Journal of Business Intelligence and Data Mining","issn_l":"1743-8187","issn":["1743-8187","1743-8195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Business Intelligence and Data Mining","raw_type":"journal-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/A5112887609","display_name":"Thanh Son Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148201","display_name":"Ho Chi Minh City University of Technology and Education","ror":"https://ror.org/05hzn5427","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210148201"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Thanh Son Nguyen","raw_affiliation_strings":["Faculty of Information Technology, Ho Chi Minh City University of Technology and Education, 1 Vo Van Ngan Street, Thuduc District, HCM City, Vietnam"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, Ho Chi Minh City University of Technology and Education, 1 Vo Van Ngan Street, Thuduc District, HCM City, Vietnam","institution_ids":["https://openalex.org/I4210148201"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5112887609"],"corresponding_institution_ids":["https://openalex.org/I4210148201"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02677154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":"1","first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T11106","display_name":"Data Management and Algorithms","score":0.9943000078201294,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9897000193595886,"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":0.8884752988815308},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.796769380569458},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.7084109783172607},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6816534996032715},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6250226497650146},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.6238541007041931},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6064354181289673},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5787320733070374},{"id":"https://openalex.org/keywords/pattern-matching","display_name":"Pattern matching","score":0.541803777217865},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5385123491287231},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5288815498352051},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.524084746837616},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.5068266987800598},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.4981851577758789},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.47802552580833435},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44867679476737976},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.43248677253723145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41764628887176514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34630852937698364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30241745710372925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18165668845176697},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14444157481193542},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13138160109519958}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.8884752988815308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.796769380569458},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.7084109783172607},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6816534996032715},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6250226497650146},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.6238541007041931},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6064354181289673},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5787320733070374},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.541803777217865},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5385123491287231},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5288815498352051},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.524084746837616},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.5068266987800598},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.4981851577758789},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.47802552580833435},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44867679476737976},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.43248677253723145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41764628887176514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34630852937698364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30241745710372925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18165668845176697},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14444157481193542},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13138160109519958},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1504/ijbidm.2017.10004687","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijbidm.2017.10004687","pdf_url":null,"source":{"id":"https://openalex.org/S47982532","display_name":"International Journal of Business Intelligence and Data Mining","issn_l":"1743-8187","issn":["1743-8187","1743-8195"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Business Intelligence and Data Mining","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W1513731586","https://openalex.org/W1525790379","https://openalex.org/W1548890319","https://openalex.org/W1552107923","https://openalex.org/W1574463154","https://openalex.org/W1604617494","https://openalex.org/W1871505182","https://openalex.org/W1980007774","https://openalex.org/W1981407057","https://openalex.org/W1996605950","https://openalex.org/W2006783944","https://openalex.org/W2011227258","https://openalex.org/W2012079387","https://openalex.org/W2020354042","https://openalex.org/W2034101327","https://openalex.org/W2037883917","https://openalex.org/W2050141939","https://openalex.org/W2052345845","https://openalex.org/W2056901498","https://openalex.org/W2058117949","https://openalex.org/W2066796814","https://openalex.org/W2067496326","https://openalex.org/W2073640212","https://openalex.org/W2079554354","https://openalex.org/W2079646556","https://openalex.org/W2081717961","https://openalex.org/W2091921805","https://openalex.org/W2093768254","https://openalex.org/W2105369890","https://openalex.org/W2109291380","https://openalex.org/W2116174583","https://openalex.org/W2118269922","https://openalex.org/W2121044532","https://openalex.org/W2128160875","https://openalex.org/W2132050943","https://openalex.org/W2136021506","https://openalex.org/W2136340804","https://openalex.org/W2137089646","https://openalex.org/W2141248505","https://openalex.org/W2151135734","https://openalex.org/W2156250920","https://openalex.org/W2281536933","https://openalex.org/W2333252900","https://openalex.org/W2415111748","https://openalex.org/W2515517980"],"related_works":["https://openalex.org/W2513074791","https://openalex.org/W3118503757","https://openalex.org/W4281630436","https://openalex.org/W2014214435","https://openalex.org/W2182136398","https://openalex.org/W1982925424","https://openalex.org/W3141827490","https://openalex.org/W2052451333","https://openalex.org/W2902282441","https://openalex.org/W3111740253"],"abstract_inverted_index":{"Time":[0],"series":[1,40,128],"forecasting":[2,56,92,129],"based":[3],"on":[4,43,55,126],"pattern":[5,69,110,135],"matching":[6,111],"has":[7],"received":[8],"a":[9,74,90],"lot":[10],"of":[11,30,47,77,147],"interest":[12],"in":[13,86,112,131,145],"recent":[14],"years":[15],"due":[16],"to":[17,23,33,88,133],"its":[18],"simplicity":[19],"and":[20,51,82],"the":[21,31,35,44,48,62,103,134],"ability":[22],"predict":[24],"complex":[25],"nonlinear":[26],"behaviours.":[27],"The":[28,115],"choice":[29],"metric":[32],"measure":[34],"similarity":[36],"between":[37],"two":[38,78,95,104],"time":[39,84,113,127,143],"depends":[41],"mainly":[42],"specific":[45],"features":[46],"considered":[49],"data":[50],"it":[52],"can":[53,122],"influence":[54],"results.":[57],"In":[58],"this":[59],"paper,":[60],"unlike":[61],"conventional":[63],"method,":[64],"we":[65],"propose":[66],"an":[67],"improved":[68],"matching-based":[70,136],"prediction":[71,148],"method":[72,137],"using":[73],"linear":[75],"combination":[76],"measures,":[79],"Euclidean":[80,139],"distance":[81,96,140],"dynamic":[83,142],"warping,":[85],"order":[87],"achieve":[89],"better":[91,124],"result.":[93],"These":[94],"measures":[97],"are":[98,102],"chosen":[99],"because":[100],"they":[101],"most":[105],"commonly":[106],"used":[107],"metrics":[108],"for":[109],"series.":[114],"experimental":[116],"results":[117,125],"showed":[118],"that":[119],"our":[120],"approach":[121],"produce":[123],"work":[130],"comparison":[132],"under":[138],"or":[141],"warping":[144],"terms":[146],"accuracy.":[149]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}