{"id":"https://openalex.org/W4396675864","doi":"https://doi.org/10.1007/s11063-024-11622-z","title":"Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM","display_name":"Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM","publication_year":2024,"publication_date":"2024-05-06","ids":{"openalex":"https://openalex.org/W4396675864","doi":"https://doi.org/10.1007/s11063-024-11622-z"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-024-11622-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11622-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11622-z.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11622-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055314266","display_name":"Sameer Poongadan","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]},{"id":"https://openalex.org/I2800415795","display_name":"Almas Hospital","ror":"https://ror.org/01tsqk383","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I2800415795"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sameer Poongadan","raw_affiliation_strings":["Department of Mathematics, National Institute of Technology Calicut, Calicut, Kerala, 673601, India","Department of Mathematics, P.S.M.O College, Tirurangadi, Malappuram, Kerala, 676306, India"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, National Institute of Technology Calicut, Calicut, Kerala, 673601, India","institution_ids":["https://openalex.org/I114845381"]},{"raw_affiliation_string":"Department of Mathematics, P.S.M.O College, Tirurangadi, Malappuram, Kerala, 676306, India","institution_ids":["https://openalex.org/I2800415795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013989385","display_name":"M. C. Lineesh","orcid":null},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. C. Lineesh","raw_affiliation_strings":["Department of Mathematics, National Institute of Technology Calicut, Calicut, Kerala, 673601, India"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, National Institute of Technology Calicut, Calicut, Kerala, 673601, India","institution_ids":["https://openalex.org/I114845381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055314266"],"corresponding_institution_ids":["https://openalex.org/I114845381","https://openalex.org/I2800415795"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":13.4722,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.98998931,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"56","issue":"3","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":0.9997000098228455,"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":0.9997000098228455,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9961000084877014,"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/singular-value-decomposition","display_name":"Singular value decomposition","score":0.7676670551300049},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5948951244354248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4884023070335388},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4848421812057495},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.48059436678886414},{"id":"https://openalex.org/keywords/singular-spectrum-analysis","display_name":"Singular spectrum analysis","score":0.46493005752563477},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44756853580474854},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.436939001083374},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4225742220878601},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21830248832702637}],"concepts":[{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.7676670551300049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5948951244354248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4884023070335388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4848421812057495},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.48059436678886414},{"id":"https://openalex.org/C136272165","wikidata":"https://www.wikidata.org/wiki/Q4048889","display_name":"Singular spectrum analysis","level":3,"score":0.46493005752563477},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44756853580474854},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.436939001083374},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4225742220878601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21830248832702637},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s11063-024-11622-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11622-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11622-z.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s11063-024-11622-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-024-11622-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-024-11622-z.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4000000059604645,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4396675864.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1586335931","https://openalex.org/W1967444754","https://openalex.org/W1971129545","https://openalex.org/W1973058694","https://openalex.org/W1987728022","https://openalex.org/W1996045017","https://openalex.org/W2004790933","https://openalex.org/W2007221293","https://openalex.org/W2009465763","https://openalex.org/W2028702910","https://openalex.org/W2050700668","https://openalex.org/W2064675550","https://openalex.org/W2085111629","https://openalex.org/W2085987121","https://openalex.org/W2101758271","https://openalex.org/W2120018700","https://openalex.org/W2120390927","https://openalex.org/W2124776405","https://openalex.org/W2125056386","https://openalex.org/W2142920810","https://openalex.org/W2153667436","https://openalex.org/W2344053498","https://openalex.org/W2406799407","https://openalex.org/W2512209472","https://openalex.org/W2560370080","https://openalex.org/W2590626818","https://openalex.org/W2622826443","https://openalex.org/W2780248324","https://openalex.org/W2786031273","https://openalex.org/W2901783186","https://openalex.org/W2963914175","https://openalex.org/W3143026122","https://openalex.org/W4229706427","https://openalex.org/W4230715394","https://openalex.org/W4383899683","https://openalex.org/W4388654875"],"related_works":["https://openalex.org/W2038393145","https://openalex.org/W1423493503","https://openalex.org/W2099549249","https://openalex.org/W1997246199","https://openalex.org/W2024695206","https://openalex.org/W2151223736","https://openalex.org/W2187338439","https://openalex.org/W932133601","https://openalex.org/W2100885243","https://openalex.org/W2038097457"],"abstract_inverted_index":{"Abstract":[0],"This":[1],"study":[2],"recommends":[3],"a":[4,79],"new":[5],"time":[6],"series":[7,72],"forecasting":[8],"model,":[9,16,138,142,146,150,156],"namely":[10,55,136],"ICEEMDAN":[11,56,67,157],"-":[12,14,140,144,148,152,154,158,163,165],"SVD":[13,58,82,153,164],"LSTM":[15,61,96,137,141,145,149,155,159,166],"which":[17],"coalesces":[18],"Improved":[19],"Complete":[20],"Ensemble":[21],"EMD":[22,139],"with":[23,78,132],"Adaptive":[24],"Noise,":[25],"Singular":[26],"Value":[27],"Decomposition":[28],"and":[29,42,60,94,103,123,161],"Long":[30],"Short":[31],"Term":[32],"Memory":[33],"network.":[34],"It":[35],"can":[36],"be":[37],"applied":[38],"to":[39,68,172],"analyse":[40],"Non-linear":[41],"non-stationary":[43],"data.":[44],"The":[45,63,81,127,168],"framework":[46],"of":[47,52,90,113,119,175],"this":[48],"model":[49,129,160,178],"is":[50,130],"comprised":[51],"three":[53],"levels,":[54],"level,":[57],"level":[59,65,86],"level.":[62,107],"first":[64],"utilized":[66],"break":[69],"up":[70],"the":[71,84,99,110,114,117,173,176,180],"into":[73],"some":[74],"IMF":[75,92,101,121],"components":[76,102,122],"along":[77],"residue.":[80,95],"in":[83,105],"second":[85],"accounts":[87],"for":[88],"de-noising":[89],"every":[91],"component":[93],"forecasts":[97],"all":[98,120],"resultant":[100],"residue":[104,124],"third":[106],"To":[108],"obtain":[109],"forecasted":[111],"values":[112],"original":[115],"data,":[116],"predictions":[118],"are":[125],"added.":[126],"proposed":[128],"contrasted":[131],"other":[133],"extant":[134],"ones,":[135],"EEMD":[143,151],"CEEMDAN":[147,162],"model.":[167],"comparison":[169],"bears":[170],"witness":[171],"potential":[174],"recommended":[177],"over":[179],"traditional":[181],"models.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
