{"id":"https://openalex.org/W3091632020","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206832","title":"Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price","display_name":"Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091632020","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206832","mag":"3091632020"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206832","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5004379460","display_name":"Zhiwen Zeng","orcid":"https://orcid.org/0000-0002-8566-6965"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Zhiwen Zeng","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030777395","display_name":"Matloob Khushi","orcid":"https://orcid.org/0000-0001-7792-2327"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Matloob Khushi","raw_affiliation_strings":["School of Computer Science, The University of Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5004379460"],"corresponding_institution_ids":["https://openalex.org/I129604602"],"apc_list":null,"apc_paid":null,"fwci":5.1995,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.95785133,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9991999864578247,"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":0.9991999864578247,"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/T11059","display_name":"Market Dynamics and Volatility","score":0.9916999936103821,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.8023529052734375},{"id":"https://openalex.org/keywords/foreign-exchange-market","display_name":"Foreign exchange market","score":0.774898886680603},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5747660994529724},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.572683572769165},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5500333905220032},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5022132396697998},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.49261170625686646},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4816074073314667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4507083296775818},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4340735375881195},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3693088889122009},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30670464038848877},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24316072463989258},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22629371285438538},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.14955487847328186},{"id":"https://openalex.org/keywords/exchange-rate","display_name":"Exchange rate","score":0.1374729871749878},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.11774170398712158}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.8023529052734375},{"id":"https://openalex.org/C536366893","wikidata":"https://www.wikidata.org/wiki/Q66076","display_name":"Foreign exchange market","level":3,"score":0.774898886680603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5747660994529724},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.572683572769165},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5500333905220032},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5022132396697998},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.49261170625686646},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4816074073314667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4507083296775818},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4340735375881195},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3693088889122009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30670464038848877},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24316072463989258},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22629371285438538},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.14955487847328186},{"id":"https://openalex.org/C2776988154","wikidata":"https://www.wikidata.org/wiki/Q66100","display_name":"Exchange rate","level":2,"score":0.1374729871749878},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.11774170398712158}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206832","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206832","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W339356811","https://openalex.org/W1069790386","https://openalex.org/W1969852690","https://openalex.org/W1972310346","https://openalex.org/W1975142646","https://openalex.org/W1976604713","https://openalex.org/W1999591167","https://openalex.org/W2001529900","https://openalex.org/W2025291942","https://openalex.org/W2045925772","https://openalex.org/W2064675550","https://openalex.org/W2117014758","https://openalex.org/W2125684070","https://openalex.org/W2129623024","https://openalex.org/W2133564696","https://openalex.org/W2154316219","https://openalex.org/W2155135527","https://openalex.org/W2293354547","https://openalex.org/W2393365513","https://openalex.org/W2470673105","https://openalex.org/W2586112617","https://openalex.org/W2613328025","https://openalex.org/W2626778328","https://openalex.org/W2741908740","https://openalex.org/W2751125473","https://openalex.org/W2769305693","https://openalex.org/W2779118454","https://openalex.org/W2800026370","https://openalex.org/W2891692146","https://openalex.org/W2909342716","https://openalex.org/W2952042565","https://openalex.org/W2965771985","https://openalex.org/W2994072953","https://openalex.org/W3091635796","https://openalex.org/W4385245566","https://openalex.org/W6679434410","https://openalex.org/W6739901393","https://openalex.org/W6743584807"],"related_works":["https://openalex.org/W3080406149","https://openalex.org/W4285321763","https://openalex.org/W3175321409","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W4312309719","https://openalex.org/W2974356760","https://openalex.org/W2980748541","https://openalex.org/W3115491726"],"abstract_inverted_index":{"Every":[0],"change":[1],"of":[2,21,39,114,120,128,149,162],"trend":[3],"in":[4,28,103],"the":[5,36,40,44,65,84,87,92,98,104,111,115,121,124,134,144,150],"forex":[6,22,41,135],"market":[7,42],"presents":[8],"a":[9,15,25,60,159],"great":[10],"opportunity":[11],"as":[12,14,133],"well":[13,109],"risk":[16],"for":[17],"investors.":[18],"Accurate":[19],"forecasting":[20],"prices":[23],"is":[24,126],"crucial":[26],"element":[27],"any":[29],"effective":[30],"hedging":[31],"or":[32],"speculation":[33],"strategy.":[34],"However,":[35],"complex":[37],"nature":[38],"makes":[43],"predicting":[45],"problem":[46],"challenging,":[47],"which":[48],"has":[49],"prompted":[50],"extensive":[51],"research":[52],"from":[53,86],"various":[54],"academic":[55],"disciplines.":[56],"In":[57],"this":[58],"paper,":[59],"novel":[61],"approach":[62,152],"that":[63],"integrates":[64],"wavelet":[66],"denoising,":[67],"Attention-based":[68],"Recurrent":[69],"Neural":[70],"Network":[71],"(ARNN),":[72],"and":[73,100,106],"Autoregressive":[74],"Integrated":[75],"Moving":[76],"Average":[77],"(ARIMA)":[78],"are":[79],"proposed.":[80],"Wavelet":[81],"transform":[82],"removes":[83],"noise":[85],"time":[88],"series":[89],"to":[90,155],"stabilize":[91],"data":[93,142],"structure.":[94],"ARNN":[95],"model":[96],"captures":[97],"robust":[99],"non-linear":[101],"relationships":[102],"sequence":[105],"ARIMA":[107],"can":[108],"fit":[110],"linear":[112],"correlation":[113],"sequential":[116],"information.":[117],"By":[118],"hybridization":[119],"three":[122],"models,":[123],"methodology":[125],"capable":[127],"modelling":[129],"dynamic":[130],"systems":[131],"such":[132],"market.":[136],"Our":[137],"experiments":[138],"on":[139],"USD/JPY":[140],"five-minute":[141],"outperforms":[143],"baseline":[145],"methods.":[146],"Root-Mean-Squared-Error":[147],"(RMSE)":[148],"hybrid":[151],"was":[153],"found":[154],"be":[156],"1.65":[157],"with":[158],"directional":[160],"accuracy":[161],"~76%.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
