{"id":"https://openalex.org/W7225268651","doi":"https://doi.org/10.20965/ijat.2026.p0595","title":"Gated Hybrid Network Using CRNN and RNN Models for Time-Series Forecasting and Case Studies","display_name":"Gated Hybrid Network Using CRNN and RNN Models for Time-Series Forecasting and Case Studies","publication_year":2026,"publication_date":"2026-11-04","ids":{"openalex":"https://openalex.org/W7225268651","doi":"https://doi.org/10.20965/ijat.2026.p0595"},"language":"en","primary_location":{"id":"doi:10.20965/ijat.2026.p0595","doi":"https://doi.org/10.20965/ijat.2026.p0595","is_oa":true,"landing_page_url":"https://doi.org/10.20965/ijat.2026.p0595","pdf_url":null,"source":{"id":"https://openalex.org/S2764525154","display_name":"International Journal of Automation Technology","issn_l":"1881-7629","issn":["1881-7629","1883-8022"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-c","cwts-core","doaj","dongbi-c","norway-1","poland-70","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4324309662","host_organization_name":"Fuji Technology Press Ltd.","host_organization_lineage":["https://openalex.org/P4324309662"],"host_organization_lineage_names":["Fuji Technology Press Ltd."],"type":"journal"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Automation Technology","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.20965/ijat.2026.p0595","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080520013","display_name":"Takayuki Kataoka","orcid":"https://orcid.org/0009-0003-8878-3715","observed_orcids":["https://orcid.org/0009-0003-8878-3715"]},"institutions":[{"id":"https://openalex.org/I916559398","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64","country_code":"JP","type":"education","lineage":["https://openalex.org/I916559398"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takayuki Kataoka","raw_affiliation_strings":["Kindai University, 1 Takaya Umenobe, Higashihiroshima, Hiroshima 739-2116, Japan"],"raw_orcid":"https://orcid.org/0009-0003-8878-3715","affiliations":[{"raw_affiliation_string":"Kindai University, 1 Takaya Umenobe, Higashihiroshima, Hiroshima 739-2116, Japan","institution_ids":["https://openalex.org/I916559398"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132271347","display_name":"Tomonari Yomono","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I126913320","display_name":"Dai Nippon Printing (Japan)","ror":"https://ror.org/01be2k939","country_code":"JP","type":"company","lineage":["https://openalex.org/I126913320"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tomonari Yomono","raw_affiliation_strings":["Dai Nippon Printing Co., Ltd., Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dai Nippon Printing Co., Ltd., Tokyo, Japan","institution_ids":["https://openalex.org/I126913320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132141449","display_name":"\u7fd4 \u5c71\u5143","orcid":"https://orcid.org/0000-0003-2987-6172","observed_orcids":["https://orcid.org/0000-0003-2987-6172"]},"institutions":[{"id":"https://openalex.org/I916559398","display_name":"Kindai University","ror":"https://ror.org/05kt9ap64","country_code":"JP","type":"education","lineage":["https://openalex.org/I916559398"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Sho Yamamoto","raw_affiliation_strings":["Kindai University, Higashiosaka, Japan"],"raw_orcid":"https://orcid.org/0000-0003-2987-6172","affiliations":[{"raw_affiliation_string":"Kindai University, Higashiosaka, Japan","institution_ids":["https://openalex.org/I916559398"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160678118","display_name":"Takashi Nishi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1282569916","display_name":"Mazda Motor Corporation (Japan)","ror":"https://ror.org/05hk1wv51","country_code":"JP","type":"company","lineage":["https://openalex.org/I1282569916"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takashi Nishi","raw_affiliation_strings":["Mazda Motor Corporation, Fuchu-cho, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mazda Motor Corporation, Fuchu-cho, Japan","institution_ids":["https://openalex.org/I1282569916"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080520013","https://openalex.org/A5132141449","https://openalex.org/A5132271347","https://openalex.org/A5160678118"],"corresponding_institution_ids":["https://openalex.org/I126913320","https://openalex.org/I1282569916","https://openalex.org/I916559398"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.84055611,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"6","first_page":"595","last_page":"606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.6435999870300293,"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":0.6435999870300293,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.3118000030517578,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.03420000150799751,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.9470000267028809},{"id":"https://openalex.org/keywords/convolutional-recurrent-neural-network","display_name":"convolutional recurrent neural network","score":0.9909999966621399},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.9729999899864197},{"id":"https://openalex.org/keywords/feature-vector-fusion","display_name":"feature vector fusion","score":0.7419999837875366},{"id":"https://openalex.org/keywords/inventory-management","display_name":"inventory management","score":0.6389999985694885},{"id":"https://openalex.org/keywords/energy-planning","display_name":"energy planning","score":0.8429999947547913},{"id":"https://openalex.org/keywords/load-forecasting","display_name":"load forecasting","score":0.8180000185966492},{"id":"https://openalex.org/keywords/solar-power-forecasting","display_name":"photovoltaic power forecasting","score":0.9810000061988831}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7523999810218811},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7006999850273132},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47530001401901245},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.41749998927116394},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.34869998693466187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3474000096321106},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3391000032424927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.20965/ijat.2026.p0595","doi":"https://doi.org/10.20965/ijat.2026.p0595","is_oa":true,"landing_page_url":"https://doi.org/10.20965/ijat.2026.p0595","pdf_url":null,"source":{"id":"https://openalex.org/S2764525154","display_name":"International Journal of Automation Technology","issn_l":"1881-7629","issn":["1881-7629","1883-8022"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-c","cwts-core","doaj","dongbi-c","norway-1","poland-70","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4324309662","host_organization_name":"Fuji Technology Press Ltd.","host_organization_lineage":["https://openalex.org/P4324309662"],"host_organization_lineage_names":["Fuji Technology Press Ltd."],"type":"journal"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Automation Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.20965/ijat.2026.p0595","doi":"https://doi.org/10.20965/ijat.2026.p0595","is_oa":true,"landing_page_url":"https://doi.org/10.20965/ijat.2026.p0595","pdf_url":null,"source":{"id":"https://openalex.org/S2764525154","display_name":"International Journal of Automation Technology","issn_l":"1881-7629","issn":["1881-7629","1883-8022"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-c","cwts-core","doaj","dongbi-c","norway-1","poland-70","russia-white-list-3","vabb-shw"],"host_organization":"https://openalex.org/P4324309662","host_organization_name":"Fuji Technology Press Ltd.","host_organization_lineage":["https://openalex.org/P4324309662"],"host_organization_lineage_names":["Fuji Technology Press Ltd."],"type":"journal"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Automation Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.7207000255584717,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1586335931","https://openalex.org/W1686810756","https://openalex.org/W1924770834","https://openalex.org/W2016287239","https://openalex.org/W2064675550","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2525778437","https://openalex.org/W2591013610","https://openalex.org/W2752782242","https://openalex.org/W2916979304","https://openalex.org/W2948490758","https://openalex.org/W2964121744","https://openalex.org/W2964199361","https://openalex.org/W3012079496","https://openalex.org/W3017116930","https://openalex.org/W3027495661","https://openalex.org/W3090661556","https://openalex.org/W3096823089","https://openalex.org/W4220674648","https://openalex.org/W4289768504","https://openalex.org/W4313473147","https://openalex.org/W4413402111","https://openalex.org/W7118144756"],"related_works":["https://openalex.org/W7215964171","https://openalex.org/W4405417127","https://openalex.org/W2895979207","https://openalex.org/W4399292332","https://openalex.org/W4415969923","https://openalex.org/W4221160313","https://openalex.org/W7160409494","https://openalex.org/W4403023816","https://openalex.org/W4411143789","https://openalex.org/W3212061040"],"abstract_inverted_index":{"Time-series":[0],"forecasting":[1,25,118,163],"is":[2,158],"a":[3,19],"critical":[4],"challenge":[5],"in":[6,55],"industrial":[7,162],"applications":[8],"such":[9],"as":[10],"inventory":[11],"management":[12],"and":[13,37,60,79,97,110,133],"energy":[14,99],"planning.":[15],"This":[16],"paper":[17],"proposes":[18],"gated":[20,129],"hybrid":[21],"network":[22,35,40],"for":[23,160],"time-series":[24,53],"that":[26,154],"combines":[27],"feature":[28,83],"vectors":[29],"obtained":[30],"from":[31],"convolutional":[32],"recurrent":[33,38,130],"neural":[34,39],"(CRNN)":[36],"(RNN)":[41],"branches.":[42],"Unlike":[43],"conventional":[44],"CRNN":[45,59],"models,":[46],"the":[47,51,81,143,155],"proposed":[48,102,156],"model":[49,103],"feeds":[50],"original":[52],"sequence":[54],"parallel":[56],"to":[57,67],"both":[58],"independent":[61],"RNN":[62],"branches,":[63],"enabling":[64],"each":[65],"branch":[66],"extract":[68],"complementary":[69],"temporal":[70],"features.":[71],"A":[72],"squeeze-and-excitation-inspired":[73],"gating":[74],"mechanism":[75],"then":[76],"adaptively":[77],"weights":[78],"integrates":[80],"two":[82],"vectors.":[84],"Experiments":[85],"were":[86],"conducted":[87],"using":[88],"six":[89],"real-world":[90],"datasets,":[91,144],"including":[92],"automotive":[93],"sales,":[94],"electricity":[95],"consumption,":[96],"solar":[98],"data.":[100],"The":[101],"achieved":[104],"lower":[105],"mean":[106],"relative":[107,111],"squared":[108],"error":[109,113],"absolute":[112],"values":[114],"averaged":[115],"across":[116],"four":[117],"periods":[119],"than":[120],"autoregressive":[121,123],"(AR),":[122],"integrated":[124],"moving":[125],"average":[126],"(ARIMA),":[127],"Holt\u2013Winters,":[128],"unit":[131],"(GRU),":[132],"long":[134],"short-term":[135],"memory":[136],"(LSTM)":[137],"baseline":[138],"models":[139],"on":[140],"most":[141],"of":[142],"while":[145],"also":[146],"demonstrating":[147],"superior":[148],"prediction":[149],"stability.":[150],"These":[151],"results":[152],"suggest":[153],"architecture":[157],"suitable":[159],"practical":[161],"tasks.":[164]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-11-05T00:00:00"}
