{"id":"https://openalex.org/W7225344728","doi":"https://doi.org/10.3389/fdata.2026.1941665","title":"A multi-method explainable AI for analyzing hybrid SARIMAX-GRU temperature forecasting in high-temperature data centers","display_name":"A multi-method explainable AI for analyzing hybrid SARIMAX-GRU temperature forecasting in high-temperature data centers","publication_year":2026,"publication_date":"2026-11-05","ids":{"openalex":"https://openalex.org/W7225344728","doi":"https://doi.org/10.3389/fdata.2026.1941665"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2026.1941665","doi":"https://doi.org/10.3389/fdata.2026.1941665","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2026.1941665","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2026.1941665/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2026.1941665/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049797571","display_name":"Safaa Hriez","orcid":"https://orcid.org/0000-0002-0340-2721","observed_orcids":["https://orcid.org/0000-0002-0340-2721"]},"institutions":[{"id":"https://openalex.org/I4405260986","display_name":"Al Hussein Technical University","ror":"https://ror.org/04jvdr241","country_code":"JO","type":"education","lineage":["https://openalex.org/I4405260986"]}],"countries":["JO"],"is_corresponding":true,"raw_author_name":"Safaa Hriez","raw_affiliation_strings":["Cyber Security Department, Al Hussein Technical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cyber Security Department, Al Hussein Technical University","institution_ids":["https://openalex.org/I4405260986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049797571"],"corresponding_institution_ids":["https://openalex.org/I4405260986"],"apc_list":{"value":1175,"currency":"CHF","value_usd":1418},"apc_paid":{"value":1175,"currency":"CHF","value_usd":1418},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.007699999958276749,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14098","display_name":"Energy Efficiency in Computing","score":0.004000000189989805,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/explainable-ai","display_name":"explainable AI","score":0.8420000076293945},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8190000057220459},{"id":"https://openalex.org/keywords/lime","display_name":"lime","score":0.996999979019165},{"id":"https://openalex.org/keywords/integrated-gradients","display_name":"integrated gradients","score":0.9779999852180481},{"id":"https://openalex.org/keywords/permutation-feature-importance","display_name":"permutation importance","score":1.0},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.6579999923706055},{"id":"https://openalex.org/keywords/residual-features","display_name":"residual features","score":0.6029999852180481},{"id":"https://openalex.org/keywords/thermal-dynamics","display_name":"thermal dynamics","score":0.6039999723434448},{"id":"https://openalex.org/keywords/energy-efficiency","display_name":"energy efficiency","score":0.8069999814033508},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.5249999761581421}],"concepts":[{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.5805000066757202},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5562000274658203},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4950999915599823},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4738999903202057},{"id":"https://openalex.org/C101601086","wikidata":"https://www.wikidata.org/wiki/Q3753228","display_name":"Rank correlation","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44339999556541443},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.429500013589859},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.374099999666214},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27810001373291016},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.265500009059906},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3389/fdata.2026.1941665","doi":"https://doi.org/10.3389/fdata.2026.1941665","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2026.1941665","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2026.1941665/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:731c8ccddc2f41a196a8dcf8af29e5d6","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/731c8ccddc2f41a196a8dcf8af29e5d6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 9 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fdata.2026.1941665","doi":"https://doi.org/10.3389/fdata.2026.1941665","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2026.1941665","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2026.1941665/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225344728.pdf","grobid_xml":"https://content.openalex.org/works/W7225344728.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1992589176","https://openalex.org/W2159081109","https://openalex.org/W2514219442","https://openalex.org/W2756249281","https://openalex.org/W2891503716","https://openalex.org/W2911964244","https://openalex.org/W4286449576","https://openalex.org/W4312200505","https://openalex.org/W4391515354","https://openalex.org/W4391530352","https://openalex.org/W4392114266","https://openalex.org/W4406707176","https://openalex.org/W4408073200","https://openalex.org/W4408320897","https://openalex.org/W4417003197","https://openalex.org/W7117542519","https://openalex.org/W7119519195"],"related_works":["https://openalex.org/W7119519195","https://openalex.org/W4409901527","https://openalex.org/W7196923156","https://openalex.org/W7223121307","https://openalex.org/W7117542519","https://openalex.org/W4411301730","https://openalex.org/W4407510142","https://openalex.org/W7135223342","https://openalex.org/W4413406773","https://openalex.org/W7117978091"],"abstract_inverted_index":{"Introduction":[0],"Accurate":[1],"temperature":[2,97,298],"prediction":[3],"is":[4],"essential":[5],"for":[6],"improving":[7],"energy":[8,321],"efficiency":[9],"and":[10,56,85,107,114,123,126,144,181,192,229,303,320],"ensuring":[11],"reliable":[12],"operation":[13],"in":[14,33,96,139,186,222,259,285],"modern":[15],"data":[16,308],"centers.":[17],"However,":[18],"despite":[19],"their":[20,31],"high":[21,108],"accuracy,":[22],"deep":[23],"learning":[24],"forecasting":[25,44,299],"models":[26],"often":[27],"lack":[28],"interpretability,":[29],"limiting":[30],"deployment":[32],"critical":[34],"environments.":[35],"This":[36,290],"study":[37,291],"extends":[38],"a":[39],"previously":[40],"developed":[41],"hybrid":[42,154,297],"SARIMAX-GRU":[43,155],"model":[45,156,247,301,330],"by":[46,324],"introducing":[47],"an":[48],"explainable":[49],"artificial":[50],"intelligence":[51],"(XAI)":[52],"framework":[53,68,314],"to":[54,88,132,165,178,205,270,272],"analyze":[55,89],"validate":[57],"its":[58],"decision-making":[59],"process":[60],"under":[61],"different":[62,263],"operating":[63,264],"conditions.":[64],"Methods":[65],"The":[66,99,149,173,241,257,312],"proposed":[67,313],"employs":[69],"four":[70],"XAI":[71,242,281,295],"techniques,":[72],"including":[73],"SHapley":[74],"Additive":[75],"exPlanations":[76],"(SHAP),":[77],"Local":[78],"Interpretable":[79],"Model-agnostic":[80],"Explanations":[81],"(LIME),":[82],"Integrated":[83,190,227],"Gradients,":[84,191,228],"Permutation":[86,193,230],"Importance,":[87,231],"the":[90,134,153,246,267,277,286,326],"contribution":[91],"of":[92,141],"temporal":[93],"residual":[94,161],"features":[95,175],"prediction.":[98,311],"analysis":[100,217,243],"considers":[101],"nine":[102],"scenarios":[103],"covering":[104],"low,":[105],"moderate,":[106],"temperatures":[109],"(24":[110],"\u00b0C,":[111,113],"27":[112],"30":[115],"\u00b0C)":[116],"with":[117,199,296],"workload":[118,179],"transitions":[119,180],"between":[120,136],"50%,":[121],"75%,":[122],"100%.":[124],"Pearson":[125,200],"Spearman":[127,215],"correlation":[128,216],"analyses":[129],"are":[130],"used":[131],"evaluate":[133],"agreement":[135,221,234,278],"explanation":[137],"methods":[138],"terms":[140],"importance":[142,261],"scores":[143],"feature":[145,223,260],"rankings,":[146],"respectively.":[147],"Results":[148],"results":[150],"show":[151],"that":[152,245,293],"mainly":[157],"depends":[158],"on":[159],"recent":[160],"features,":[162],"particularly":[163],"Residual_t1":[164],"Residual_t4,":[166],"while":[167,207,276],"older":[168],"residuals":[169],"have":[170],"limited":[171],"influence.":[172],"dominant":[174],"vary":[176],"according":[177],"sensor":[182],"locations,":[183],"reflecting":[184],"changes":[185],"thermal":[187,251,274,310],"dynamics.":[188],"SHAP,":[189,226],"Importance":[194],"demonstrate":[195],"highly":[196],"consistent":[197],"explanations,":[198],"correlations":[201],"ranging":[202],"from":[203],"0.93":[204],"1.00,":[206],"LIME":[208,236],"provides":[209,304],"more":[210,316],"variable":[211],"but":[212],"complementary":[213],"interpretations.":[214],"further":[218],"confirms":[219,244],"strong":[220],"rankings":[224],"among":[225,279],"whereas":[232],"rank":[233],"involving":[235],"varies":[237],"across":[238,262],"scenarios.":[239],"Discussion":[240],"captures":[248],"meaningful":[249],"short-term":[250],"dependencies":[252],"rather":[253],"than":[254],"arbitrary":[255],"patterns.":[256],"variations":[258],"conditions":[265],"highlight":[266],"model's":[268],"ability":[269],"adapt":[271],"changing":[273],"behaviors,":[275],"multiple":[280],"techniques":[282],"improves":[283],"confidence":[284],"generated":[287],"explanations.":[288],"Conclusion":[289],"demonstrates":[292],"integrating":[294],"enhances":[300],"transparency":[302],"valuable":[305],"insights":[306],"into":[307],"center":[309],"supports":[315],"trustworthy":[317],"intelligent":[318],"cooling":[319],"optimization":[322],"decisions":[323],"identifying":[325],"key":[327],"factors":[328],"influencing":[329],"predictions.":[331]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-11-06T00:00:00"}