{"id":"https://openalex.org/W7204201384","doi":"https://doi.org/10.48550/arxiv.2608.22968","title":"Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study","display_name":"Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204201384","doi":"https://doi.org/10.48550/arxiv.2608.22968"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.22968","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22968","pdf_url":"https://arxiv.org/pdf/2608.22968","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.22968","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111925886","display_name":"Guan-Hua Wen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen, Guan-Hua","raw_affiliation_strings":["Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei , Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei , Taiwan","institution_ids":["https://openalex.org/I154864474"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148137607","display_name":"Kuan-Yu Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I154864474","display_name":"National Taiwan University of Science and Technology","ror":"https://ror.org/00q09pe49","country_code":"TW","type":"education","lineage":["https://openalex.org/I154864474"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chen, Kuan-Yu","raw_affiliation_strings":["Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei , Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei , Taiwan","institution_ids":["https://openalex.org/I154864474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154864474"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7031000256538391,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.7031000256538391,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.13179999589920044,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.1298000067472458,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/time-series-foundation-models","display_name":"time series foundation models","score":0.9940000176429749},{"id":"https://openalex.org/keywords/industrial-monitoring","display_name":"industrial monitoring","score":0.9309999942779541},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.8899999856948853},{"id":"https://openalex.org/keywords/moments","display_name":"moments","score":0.8479999899864197},{"id":"https://openalex.org/keywords/timesfm","display_name":"TimesFM","score":0.9729999899864197},{"id":"https://openalex.org/keywords/deployment-cost","display_name":"deployment cost","score":0.7850000262260437},{"id":"https://openalex.org/keywords/zero-shot-forecasting","display_name":"zero-shot forecasting","score":0.5740000009536743}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8125},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6635000109672546},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.6618000268936157},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5421000123023987},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.45969998836517334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4334000051021576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4309000074863434},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41119998693466187},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.38280001282691956},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3301999866962433},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2973000109195709},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28519999980926514},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.27959999442100525},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.257999986410141}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.22968","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22968","pdf_url":"https://arxiv.org/pdf/2608.22968","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.22968","doi":"https://doi.org/10.48550/arxiv.2608.22968","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.22968","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.22968","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22968","pdf_url":"https://arxiv.org/pdf/2608.22968","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204201384.pdf","grobid_xml":"https://content.openalex.org/works/W7204201384.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2132870739","https://openalex.org/W3033693292","https://openalex.org/W4234698323","https://openalex.org/W4254182148","https://openalex.org/W4390494339","https://openalex.org/W4402056503"],"related_works":["https://openalex.org/W7222970753","https://openalex.org/W7154584612","https://openalex.org/W7155359458","https://openalex.org/W4415777467","https://openalex.org/W7168348348","https://openalex.org/W7221693269","https://openalex.org/W7160286231","https://openalex.org/W7172418568","https://openalex.org/W6908480274","https://openalex.org/W4415316386"],"abstract_inverted_index":{"Industrial":[0],"monitoring":[1],"models":[2,18],"must":[3],"detect":[4],"operationally":[5],"relevant":[6],"deviations":[7],"while":[8],"satisfying":[9],"target-specific":[10],"data,":[11],"calibration,":[12],"and":[13,23,39,65,85,97,100,146,162,176,191,201],"resource":[14],"constraints.":[15],"Time-series":[16],"foundation":[17],"(TSFMs)":[19],"promise":[20],"reusable":[21],"representations":[22],"zero-shot":[24,202],"forecasts,":[25],"yet":[26],"evidence":[27],"for":[28,60,120,130,213],"their":[29],"deployment":[30,207],"value":[31],"remains":[32],"mixed":[33],"when":[34],"task":[35],"definitions":[36],"are":[37,42,205],"heterogeneous":[38],"lightweight":[40,215],"baselines":[41],"competitive.":[43],"This":[44],"work":[45],"presents":[46],"a":[47,54,149],"protocol-aware":[48],"empirical":[49],"assessment":[50],"across":[51,174],"three":[52],"settings:":[53],"C-MAPSS":[55,106],"degradation-risk":[56],"proxy,":[57],"normal-only":[58],"training":[59],"anomalous-sound":[61],"detection":[62],"on":[63],"MIMII,":[64],"BDG2":[66,152],"forecasting-residual":[67],"diagnostics":[68],"with":[69,118],"synthetic":[70,165,170],"target":[71],"perturbations.":[72],"We":[73],"assess":[74],"classical":[75],"one-class":[76],"methods,":[77],"compact":[78],"neural":[79],"autoencoders,":[80],"residual":[81,95,178],"forecasters,":[82],"MOMENT-small,":[83],"Chronos-T5,":[84],"TimesFM":[86,154],"2.5":[87,155],"in":[88,144],"terms":[89],"of":[90,110],"anomaly-ranking":[91],"performance,":[92],"risk-horizon":[93],"sensitivity,":[94,99],"forecasting":[96],"perturbation":[98],"local":[101],"implementation":[102],"cost.":[103],"Across":[104,133],"100":[105],"engines":[107],"evaluated":[108,199],"out":[109],"fold,":[111],"TCN-AE":[112],"reaches":[113],"fold-weighted":[114],"AUROC/AUPRC":[115],"0.9570/0.8960,":[116],"compared":[117],"0.7310/0.3080":[119],"MOMENT":[121,142,184],"reconstruction;":[122],"paired":[123],"engine-cluster":[124],"bootstrap":[125],"confidence":[126],"intervals":[127],"exclude":[128],"zero":[129],"both":[131],"differences.":[132],"five":[134],"matched":[135],"MIMII":[136],"pump":[137],"evaluations,":[138],"OCSVM":[139],"also":[140],"exceeds":[141],"reconstruction":[143],"AUROC":[145,166],"AUPRC.":[147],"On":[148],"fixed":[150],"12-meter":[151],"panel,":[153],"has":[156],"the":[157,163,198],"lowest":[158],"aligned":[159],"forecast":[160],"error":[161],"highest":[164],"point":[167],"estimate,":[168],"although":[169],"AUPRC":[171],"is":[172],"similar":[173],"TSFM":[175],"fitted":[177,214],"models.":[179,216],"Same-device":[180],"measurements":[181],"show":[182],"that":[183],"incurs":[185],"higher":[186],"latency,":[187],"peak":[188],"allocated":[189],"VRAM,":[190],"serialized":[192],"state-dictionary":[193],"size":[194],"than":[195,210],"TCN-AE.":[196],"Under":[197],"frozen":[200],"settings,":[203],"TSFMs":[204],"task-dependent":[206],"options":[208],"rather":[209],"default":[211],"replacements":[212]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-26T00:00:00"}
