{"id":"https://openalex.org/W4412999687","doi":"https://doi.org/10.1142/s0219649225500625","title":"ICAIMT: ART-LSTM: Augmented Reverse Training for Data-Efficient Time Series Forecasting","display_name":"ICAIMT: ART-LSTM: Augmented Reverse Training for Data-Efficient Time Series Forecasting","publication_year":2025,"publication_date":"2025-08-04","ids":{"openalex":"https://openalex.org/W4412999687","doi":"https://doi.org/10.1142/s0219649225500625"},"language":"en","primary_location":{"id":"doi:10.1142/s0219649225500625","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219649225500625","pdf_url":null,"source":{"id":"https://openalex.org/S30163770","display_name":"Journal of Information & Knowledge Management","issn_l":"0219-6492","issn":["0219-6492","1793-6926"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information &amp; Knowledge Management","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/A5051609871","display_name":"Firuz Kamalov","orcid":"https://orcid.org/0000-0003-3946-0920"},"institutions":[{"id":"https://openalex.org/I186129607","display_name":"Canadian University of Dubai","ror":"https://ror.org/029zgsn59","country_code":"AE","type":"education","lineage":["https://openalex.org/I186129607"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Firuz Kamalov","raw_affiliation_strings":["Canadian University Dubai, UAE"],"raw_orcid":"https://orcid.org/0000-0003-3946-0920","affiliations":[{"raw_affiliation_string":"Canadian University Dubai, UAE","institution_ids":["https://openalex.org/I186129607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030399410","display_name":"Ikhlaas Gurrib","orcid":"https://orcid.org/0000-0001-8393-9790"},"institutions":[{"id":"https://openalex.org/I186129607","display_name":"Canadian University of Dubai","ror":"https://ror.org/029zgsn59","country_code":"AE","type":"education","lineage":["https://openalex.org/I186129607"]}],"countries":["AE"],"is_corresponding":true,"raw_author_name":"Ikhlaas Gurrib","raw_affiliation_strings":["Canadian University Dubai, UAE"],"raw_orcid":"https://orcid.org/0000-0001-8393-9790","affiliations":[{"raw_affiliation_string":"Canadian University Dubai, UAE","institution_ids":["https://openalex.org/I186129607"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065159041","display_name":"Linda Smail","orcid":"https://orcid.org/0000-0001-9388-1334"},"institutions":[{"id":"https://openalex.org/I91044093","display_name":"Zayed University","ror":"https://ror.org/03snqfa66","country_code":"AE","type":"education","lineage":["https://openalex.org/I91044093"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Linda Smail","raw_affiliation_strings":["Zayed University, UAE"],"raw_orcid":"https://orcid.org/0000-0001-9388-1334","affiliations":[{"raw_affiliation_string":"Zayed University, UAE","institution_ids":["https://openalex.org/I91044093"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ziad El Khatib","orcid":"https://orcid.org/0000-0003-0756-7280"},"institutions":[{"id":"https://openalex.org/I186129607","display_name":"Canadian University of Dubai","ror":"https://ror.org/029zgsn59","country_code":"AE","type":"education","lineage":["https://openalex.org/I186129607"]}],"countries":["AE"],"is_corresponding":false,"raw_author_name":"Ziad El Khatib","raw_affiliation_strings":["Canadian University Dubai, UAE"],"raw_orcid":"https://orcid.org/0000-0003-0756-7280","affiliations":[{"raw_affiliation_string":"Canadian University Dubai, UAE","institution_ids":["https://openalex.org/I186129607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030399410"],"corresponding_institution_ids":["https://openalex.org/I186129607"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21476663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":"06","first_page":null,"last_page":null},"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/T14319","display_name":"Currency Recognition and Detection","score":0.9821000099182129,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9817000031471252,"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/computer-science","display_name":"Computer science","score":0.7989464402198792},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7245612740516663},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6186103224754333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5948174595832825},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5677175521850586},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5593466758728027},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5397874116897583},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5318225026130676},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.47264593839645386},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.44882211089134216},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4195529520511627},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.330258309841156},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1352728009223938},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09320420026779175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7989464402198792},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7245612740516663},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6186103224754333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5948174595832825},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5677175521850586},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5593466758728027},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5397874116897583},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5318225026130676},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.47264593839645386},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.44882211089134216},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4195529520511627},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.330258309841156},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1352728009223938},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09320420026779175},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0219649225500625","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0219649225500625","pdf_url":null,"source":{"id":"https://openalex.org/S30163770","display_name":"Journal of Information & Knowledge Management","issn_l":"0219-6492","issn":["0219-6492","1793-6926"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information &amp; Knowledge Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2131774270","https://openalex.org/W2579495707","https://openalex.org/W2909877301","https://openalex.org/W2971724044","https://openalex.org/W2995317627","https://openalex.org/W3007066689","https://openalex.org/W3036196749","https://openalex.org/W3046296398","https://openalex.org/W3177318507","https://openalex.org/W4327571413","https://openalex.org/W4366828973","https://openalex.org/W4390928083","https://openalex.org/W4391361670","https://openalex.org/W4391608608","https://openalex.org/W4392775745","https://openalex.org/W4400840000","https://openalex.org/W4402766434","https://openalex.org/W4403291388","https://openalex.org/W4403834800","https://openalex.org/W4406640922"],"related_works":["https://openalex.org/W2150029999","https://openalex.org/W3175321409","https://openalex.org/W4412948621","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W2146461990","https://openalex.org/W4312309719","https://openalex.org/W4391216528","https://openalex.org/W2980748541"],"abstract_inverted_index":{"Financial":[0],"time":[1,58],"series":[2,59],"forecasting":[3,60,157],"faces":[4],"significant":[5],"challenges":[6],"due":[7],"to":[8,33],"data":[9,54,81,162],"scarcity,":[10],"high":[11],"volatility,":[12],"and":[13,27,71,95,108,127,129,141,152,164],"inherent":[14],"nonlinearities.":[15],"Complex":[16],"deep":[17],"learning":[18],"models,":[19],"such":[20],"as":[21],"transformers,":[22],"typically":[23],"require":[24],"extensive":[25],"datasets":[26,39],"computational":[28,89],"resources,":[29],"making":[30],"them":[31],"prone":[32],"overfitting":[34],"in":[35,136],"financial":[36,156],"contexts":[37],"where":[38],"are":[40],"limited.":[41],"To":[42],"address":[43],"this,":[44],"we":[45],"propose":[46],"Augmented":[47],"Reverse":[48],"Training":[49],"LSTM":[50,65],"(ART-LSTM),":[51],"a":[52,62,150],"novel":[53],"augmentation":[55],"strategy":[56],"for":[57,155],"using":[61],"straightforward":[63],"unidirectional":[64],"architecture.":[66],"ART-LSTM":[67,114,148],"leverages":[68],"both":[69],"forward":[70],"reversed":[72],"sequences":[73],"during":[74],"training,":[75],"effectively":[76],"doubling":[77],"the":[78],"available":[79],"training":[80],"without":[82],"increasing":[83],"architectural":[84],"complexity.":[85],"Our":[86],"approach":[87],"maintains":[88],"simplicity":[90],"while":[91],"enhancing":[92],"model":[93],"robustness":[94],"generalisation.":[96],"Empirical":[97],"evaluations":[98],"on":[99],"challenging":[100],"datasets,":[101],"including":[102],"daily":[103],"S&amp;P":[104],"500":[105],"index":[106],"prices":[107],"USD/EUR":[109],"exchange":[110],"rates,":[111],"demonstrate":[112],"that":[113],"consistently":[115],"outperforms":[116],"traditional":[117],"statistical":[118],"methods":[119],"(ARIMA),":[120],"standard":[121],"recurrent":[122],"neural":[123],"networks":[124],"(RNN,":[125],"GRU,":[126],"LSTM),":[128],"multi-layer":[130],"perceptrons":[131],"(MLPs),":[132],"achieving":[133],"substantial":[134],"reductions":[135],"Mean":[137,143],"Absolute":[138],"Error":[139,145],"(MAE)":[140],"Root":[142],"Squared":[144],"(RMSE).":[146],"Overall,":[147],"provides":[149],"practical":[151],"data-efficient":[153],"solution":[154],"tasks":[158],"characterised":[159],"by":[160],"limited":[161],"availability":[163],"volatile":[165],"dynamics.":[166]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
