{"id":"https://openalex.org/W7163359559","doi":"https://doi.org/10.48550/arxiv.2606.03629","title":"TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning","display_name":"TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163359559","doi":"https://doi.org/10.48550/arxiv.2606.03629"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.03629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137784190","display_name":"Shunyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shunyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137799522","display_name":"Dan Li","orcid":"https://orcid.org/0009-0002-3405-9986"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107942095","display_name":"H. Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Haozheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133569406","display_name":"Weibin Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Weibin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137743363","display_name":"Jian Lou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lou, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137724223","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-4568-8035"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052227670","display_name":"Wenjie Feng","orcid":"https://orcid.org/0000-0003-3636-0035"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Wenjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137736331","display_name":"Chenjuan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Chenjuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137749037","display_name":"See-Kiong Ng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ng, See-Kiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.7067999839782715,"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":0.7067999839782715,"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/T11719","display_name":"Data Quality and Management","score":0.16220000386238098,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.06449999660253525,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.698199987411499},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6360999941825867},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.5480999946594238},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.531000018119812},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5273000001907349},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.459199994802475},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4415000081062317}],"concepts":[{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.698199987411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6887000203132629},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6360999941825867},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.5480999946594238},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.44130000472068787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41499999165534973},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4115999937057495},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4052000045776367},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.29820001125335693},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C95986675","wikidata":"https://www.wikidata.org/wiki/Q185168","display_name":"Quantitative analysis (chemistry)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C87156501","wikidata":"https://www.wikidata.org/wiki/Q7268708","display_name":"Qualitative property","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2583000063896179},{"id":"https://openalex.org/C2779346075","wikidata":"https://www.wikidata.org/wiki/Q7268763","display_name":"Quality Score","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C71405471","wikidata":"https://www.wikidata.org/wiki/Q757012","display_name":"Quality management","level":3,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03629","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4663860499858856,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Assessing":[0],"the":[1,15,157,170,176,203],"quality":[2,19,34,49,64,71,94,99,118,134,179,221],"of":[3,18,137],"time":[4],"series":[5],"(TS)":[6],"data":[7,235,243],"is":[8],"fundamental":[9],"yet":[10],"inherently":[11],"challenging":[12],"due":[13],"to":[14,172,192,238],"multifaceted":[16],"nature":[17],"dimensions.":[20,103,199],"Recently,":[21],"large":[22],"language":[23],"models":[24],"(LLMs)":[25],"have":[26],"emerged":[27],"as":[28],"a":[29,79,127],"promising":[30],"paradigm":[31],"for":[32,82,132,142,147],"TS":[33,133],"assessment":[35],"via":[36],"pairwise":[37],"comparison":[38,100,225],"and":[39,51,69,91,96,116,151,155,174,181,206,223,242],"per-dimension":[40],"evaluation.":[41],"However,":[42],"existing":[43],"approaches":[44],"rely":[45],"on":[46,85,201],"manually":[47],"predefined":[48],"dimensions":[50,65],"purely":[52],"text-based":[53],"reasoning,":[54],"leaving":[55],"it":[56],"unknown":[57],"whether":[58],"LLMs":[59,84,109],"can":[60],"identify":[61,173],"truly":[62],"relevant":[63,93,178],"or":[66],"perform":[67],"grounded":[68],"quantitative":[70,149,195,224],"comparisons.":[72],"To":[73,120],"investigate":[74],"this,":[75],"we":[76,124,162],"construct":[77],"TSQBench,":[78],"dedicated":[80],"benchmark":[81,205],"evaluating":[83],"two":[86],"progressive":[87],"capabilities:":[88],"(i)":[89],"understanding":[90,222],"identifying":[92],"dimensions,":[95,180],"(ii)":[97],"performing":[98],"under":[101],"specific":[102],"Our":[104],"analysis":[105],"reveals":[106],"that":[107,153,168,211],"current":[108],"consistently":[110],"struggle":[111],"with":[112,188],"both":[113,202],"dimension":[114,144],"identification":[115],"evidence-grounded":[117],"comparison.":[119],"address":[121],"these":[122,230],"limitations,":[123],"propose":[125,183],"TSQAgent,":[126],"novel":[128],"agentic":[129,165],"reasoning":[130,166],"framework":[131,213],"rating":[135],"consisting":[136],"three":[138],"collaborative":[139],"roles:":[140],"Perceiver":[141],"focused":[143],"selection,":[145,236],"Inspector":[146],"dimension-wise":[148],"analysis,":[150],"Adjudicator":[152],"aggregates":[154],"refines":[156],"final":[158],"judgment.":[159],"In":[160],"particular,":[161],"introduce":[163],"an":[164,184],"strategy":[167],"instills":[169],"ability":[171],"prioritize":[175],"most":[177],"further":[182],"agent":[185],"workflow":[186],"equipped":[187],"external":[189],"analytical":[190],"tools":[191],"enable":[193],"precise":[194],"comparisons":[196],"over":[197],"selected":[198],"Experiments":[200],"proposed":[204],"eleven":[207],"real-world":[208],"datasets":[209],"demonstrate":[210],"our":[212],"not":[214],"only":[215],"substantially":[216],"improves":[217],"LLMs'":[218],"capabilities":[219],"in":[220],"but":[226],"also":[227],"effectively":[228],"translates":[229],"improvements":[231],"into":[232],"better":[233],"quality-aware":[234],"leading":[237],"enhanced":[239],"downstream":[240],"performance":[241],"efficiency.":[244]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-04T00:00:00"}
