{"id":"https://openalex.org/W3196702184","doi":"https://doi.org/10.1145/3468891.3468896","title":"Image-based Candlestick Pattern Classification with Machine Learning","display_name":"Image-based Candlestick Pattern Classification with Machine Learning","publication_year":2021,"publication_date":"2021-04-23","ids":{"openalex":"https://openalex.org/W3196702184","doi":"https://doi.org/10.1145/3468891.3468896","mag":"3196702184"},"language":"en","primary_location":{"id":"doi:10.1145/3468891.3468896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468891.3468896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Machine Learning Technologies","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100562309","display_name":"Chenghan Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I47689461","display_name":"Northeast Forestry University","ror":"https://ror.org/02yxnh564","country_code":"CN","type":"education","lineage":["https://openalex.org/I47689461"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenghan Xu","raw_affiliation_strings":["Northeast Forestry University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northeast Forestry University, China","institution_ids":["https://openalex.org/I47689461"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100562309"],"corresponding_institution_ids":["https://openalex.org/I47689461"],"apc_list":null,"apc_paid":null,"fwci":0.6761,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.66162504,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"26","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9976000189781189,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9896000027656555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.756851077079773},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7189022302627563},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6462278366088867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6350438594818115},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.558423638343811},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4843662679195404},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4831632971763611},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4820181131362915},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4369223415851593},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41342371702194214},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4120280146598816},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40190282464027405},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3449972867965698},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2844240069389343},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2286602258682251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09137985110282898}],"concepts":[{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.756851077079773},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7189022302627563},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6462278366088867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6350438594818115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.558423638343811},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4843662679195404},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4831632971763611},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4820181131362915},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4369223415851593},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41342371702194214},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4120280146598816},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40190282464027405},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3449972867965698},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2844240069389343},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2286602258682251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09137985110282898},{"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:10.1145/3468891.3468896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3468891.3468896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Machine Learning Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2899619587","https://openalex.org/W2917928566","https://openalex.org/W2969608920","https://openalex.org/W3006845699","https://openalex.org/W3012362112","https://openalex.org/W3023384820","https://openalex.org/W3032979685","https://openalex.org/W6600042794","https://openalex.org/W6601630192"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3011239835","https://openalex.org/W4312534362","https://openalex.org/W3213126983","https://openalex.org/W3185760728","https://openalex.org/W2915047625","https://openalex.org/W2011666252","https://openalex.org/W3134034502"],"abstract_inverted_index":{"Financial":[0],"markets,":[1],"such":[2],"as":[3],"the":[4,30,42,50,88,96,100,130],"stock":[5],"market,":[6,12],"bond":[7],"market":[8],"and":[9,34,82,113,119,146],"foreign":[10],"exchange":[11],"are":[13,63,115],"important":[14],"channels":[15],"for":[16,99,129],"fund":[17],"transfer.":[18],"As":[19],"a":[20,38],"graphical":[21],"analysis":[22],"tool,":[23],"candlestick":[24,103,131],"charts":[25],"use":[26,89,143],"graphs":[27],"to":[28,48,142],"display":[29],"open,":[31],"high,":[32],"low,":[33],"close":[35],"prices":[36],"in":[37],"specific":[39],"period.":[40],"In":[41,66],"past,":[43],"there":[44],"have":[45],"been":[46],"attempts":[47],"identify":[49],"characteristics":[51],"of":[52,90,102],"candlesticks":[53],"based":[54,105,135],"on":[55,106,136],"Gramian":[56],"Angular":[57],"Field":[58],"(GAF)":[59],"images,":[60,138],"but":[61,121],"they":[62],"not":[64,95],"perfect.":[65],"this":[67],"study,":[68],"we":[69,85],"implemented":[70],"Multilayer":[71],"Perceptron":[72],"(MLP),":[73],"Convolutional":[74],"Neural":[75],"Network":[76],"(CNN),":[77],"AdaBoost,":[78],"Random":[79],"Forest":[80],"(RF)":[81],"XGBoost":[83],"models,":[84,111],"found":[86],"that":[87,128],"deep":[91],"learning":[92,149],"models":[93,150],"is":[94,140],"best":[97],"choice":[98],"recognition":[101],"features":[104],"GAF":[107,137],"images.":[108],"Comparing":[109],"these":[110],"MLP":[112],"CNN":[114],"better":[116],"than":[117,123],"AdaBoost":[118],"RF,":[120],"worse":[122],"XGBoost.":[124],"Our":[125],"results":[126,155],"show":[127],"pattern":[132],"classification":[133],"problem":[134],"it":[139],"unnecessary":[141],"complex":[144],"CNNs":[145],"traditional":[147],"machine":[148],"can":[151],"also":[152],"achieve":[153],"satisfactory":[154],"with":[156],"much":[157],"less":[158],"computation":[159],"resources.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
