{"id":"https://openalex.org/W4406458474","doi":"https://doi.org/10.1109/bigdata62323.2024.10825969","title":"Time Series Forecasting with GCN-LSTM Based Unified Model for Product Demand Prediction","display_name":"Time Series Forecasting with GCN-LSTM Based Unified Model for Product Demand Prediction","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458474","doi":"https://doi.org/10.1109/bigdata62323.2024.10825969"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5111116028","display_name":"Melike Yildiz Aktas","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Melike Yildiz Aktas","raw_affiliation_strings":["Department of Computer Science,Virginia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science,Virginia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050083334","display_name":"Taoran Ji","orcid":"https://orcid.org/0000-0001-9438-3038"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taoran Ji","raw_affiliation_strings":["Texas A&amp;M University-Corpus Christi"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University-Corpus Christi","institution_ids":["https://openalex.org/I96749437"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038002204","display_name":"Chang\u2010Tien Lu","orcid":"https://orcid.org/0000-0003-3675-0199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang-Tien Lu","raw_affiliation_strings":["Department of Computer Science,Virginia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science,Virginia","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111116028"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4578,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.84963905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5901","last_page":"5906"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9933000206947327,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9933000206947327,"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":0.9801999926567078,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.95660001039505,"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":0.6662818193435669},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6607974767684937},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6028780937194824},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5597208738327026},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.5594350695610046},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.458479642868042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40200552344322205},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3426136374473572},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.1477353274822235},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1276036500930786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662818193435669},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6607974767684937},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6028780937194824},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5597208738327026},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5594350695610046},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.458479642868042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40200552344322205},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3426136374473572},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.1477353274822235},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1276036500930786},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W2024760831","https://openalex.org/W2064675550","https://openalex.org/W2085866051","https://openalex.org/W2375548658","https://openalex.org/W2807252330","https://openalex.org/W2904449562","https://openalex.org/W2921438067","https://openalex.org/W2923129012","https://openalex.org/W2952992820","https://openalex.org/W2964015378","https://openalex.org/W2967097864","https://openalex.org/W2996451395","https://openalex.org/W3014191625","https://openalex.org/W3016366056","https://openalex.org/W3016414288","https://openalex.org/W3017116930","https://openalex.org/W3022787740","https://openalex.org/W3031678578","https://openalex.org/W3046711798","https://openalex.org/W3080253043","https://openalex.org/W3119095567","https://openalex.org/W3139204882","https://openalex.org/W3167204364","https://openalex.org/W3185274067","https://openalex.org/W3190469032","https://openalex.org/W4205385003","https://openalex.org/W4229058446","https://openalex.org/W4285011934","https://openalex.org/W4297733535","https://openalex.org/W4313473147","https://openalex.org/W4388834824","https://openalex.org/W4391617606","https://openalex.org/W4401455699","https://openalex.org/W6640212811","https://openalex.org/W6726873649","https://openalex.org/W6786152982"],"related_works":["https://openalex.org/W1919101720","https://openalex.org/W4390822878","https://openalex.org/W96888382","https://openalex.org/W4386126592","https://openalex.org/W2041308758","https://openalex.org/W4392529072","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"This":[0,15],"paper":[1],"introduces":[2],"LSTMGraph,":[3],"a":[4,47,53,65,71],"unified":[5],"time-series":[6],"model":[7,37],"designed":[8],"for":[9],"demand":[10,44],"prediction":[11],"across":[12],"multiple":[13],"products.":[14,41],"method":[16],"integrates":[17],"Long":[18],"Short-Term":[19],"Memory":[20],"(LSTM)":[21],"networks":[22],"to":[23,36,85,112],"capture":[24],"temporal":[25,92],"dynamics,":[26],"such":[27],"as":[28,46],"price":[29],"fluctuations,":[30],"and":[31,75,93],"Graph":[32],"Convolutional":[33],"Networks":[34],"(GCN)":[35],"global":[38],"dependencies":[39],"between":[40],"We":[42],"represent":[43],"data":[45],"network":[48],"where":[49],"each":[50,117],"product":[51],"is":[52,110],"node,":[54],"constructing":[55],"three":[56],"distinct":[57],"graphs":[58,82],"with":[59],"different":[60],"types":[61],"of":[62,116],"edges:":[63],"(i)":[64],"weekly":[66],"sales":[67],"similarity":[68,79],"graph,":[69,74],"(ii)":[70],"customer-based":[72],"relationship":[73],"(iii)":[76],"an":[77,107],"invoice-based":[78],"graph.":[80],"These":[81],"are":[83],"merged":[84],"enhance":[86],"predictive":[87],"accuracy":[88],"by":[89],"incorporating":[90],"diverse":[91],"relational":[94],"patterns.":[95],"Extensive":[96],"experiments":[97],"show":[98],"that":[99],"LSTMGraph":[100],"significantly":[101],"outperforms":[102],"existing":[103],"baseline":[104],"models.":[105],"Additionally,":[106],"ablation":[108],"study":[109],"conducted":[111],"quantify":[113],"the":[114],"impact":[115],"graph":[118],"type":[119],"on":[120],"overall":[121],"performance.":[122]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}
