{"id":"https://openalex.org/W7137990918","doi":"https://doi.org/10.1609/aaai.v40i18.38593","title":"WaveDiST: A Wavelet Diffusion Transformer for Spatio-Temporal Estimation on Unobserved Locations","display_name":"WaveDiST: A Wavelet Diffusion Transformer for Spatio-Temporal Estimation on Unobserved Locations","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7137990918","doi":"https://doi.org/10.1609/aaai.v40i18.38593"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i18.38593","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38593","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38593/42555","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38593/42555","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104352434","display_name":"Huiling Qin","orcid":"https://orcid.org/0000-0002-4045-6091"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huiling Qin","raw_affiliation_strings":["Beijing Normal University"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129682624","display_name":"Yuanxun Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210092282","display_name":"Soft99 (Japan)","ror":"https://ror.org/00mwp7g70","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210092282"]},{"id":"https://openalex.org/I4210111308","display_name":"Soft Imaging LLC (United States)","ror":"https://ror.org/01y4fs673","country_code":"US","type":"company","lineage":["https://openalex.org/I4210111308"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Yuanxun Li","raw_affiliation_strings":["King Soft"],"affiliations":[{"raw_affiliation_string":"King Soft","institution_ids":["https://openalex.org/I4210092282","https://openalex.org/I4210111308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051803761","display_name":"Weijia Jia","orcid":"https://orcid.org/0000-0002-0231-3196"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Weijia Jia","raw_affiliation_strings":["Beijing Normal University\nBeijing Normal-Hong Kong Baptist University"],"affiliations":[{"raw_affiliation_string":"Beijing Normal University\nBeijing Normal-Hong Kong Baptist University","institution_ids":["https://openalex.org/I141568987","https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5104352434"],"corresponding_institution_ids":["https://openalex.org/I25254941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54081633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"18","first_page":"15635","last_page":"15643"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4214000105857849,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.4214000105857849,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.3767000138759613,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.014399999752640724,"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/wavelet","display_name":"Wavelet","score":0.508899986743927},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4226999878883362},{"id":"https://openalex.org/keywords/diffusion-process","display_name":"Diffusion process","score":0.4036000072956085},{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.3522999882698059},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.3456999957561493},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.3379000127315521},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.32420000433921814},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.3197000026702881},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3082999885082245}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.599399983882904},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4226999878883362},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3959999978542328},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38920000195503235},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3379000127315521},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C133770746","wikidata":"https://www.wikidata.org/wiki/Q856535","display_name":"Current transformer","level":4,"score":0.27059999108314514},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2678000032901764},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i18.38593","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38593","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38593/42555","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i18.38593","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i18.38593","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/38593/42555","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8387143611907959,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7899227572","display_name":null,"funder_award_id":"32000400","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8160964939","display_name":null,"funder_award_id":"62272050","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320955","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321432","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7137990918.pdf","grobid_xml":"https://content.openalex.org/works/W7137990918.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spatio-temporal":[0],"estimation":[1,102],"plays":[2],"a":[3,86,95,125,133,173],"vital":[4],"role":[5],"in":[6,48,90,148],"numerous":[7],"scientific":[8],"and":[9,70,117,123,157,183,193],"engineering":[10],"tasks,":[11],"particularly":[12],"for":[13,66,100],"novel":[14],"or":[15,32,77],"unobserved":[16,24,61],"locations":[17],"lacking":[18],"historical":[19,54,105],"references.":[20],"Many":[21],"areas":[22,40],"remain":[23],"by":[25],"sensors":[26],"due":[27],"to":[28,93,152],"their":[29],"non-core":[30],"location":[31],"pending":[33],"development":[34],"status.":[35],"The":[36],"states":[37,63],"of":[38,128,164],"these":[39,60],"can":[41],"only":[42],"be":[43],"estimated":[44],"through":[45,53,120],"similar":[46],"nodes":[47],"the":[49,149,162],"geospace,":[50],"rather":[51],"than":[52],"data":[55,79,107,114,131],"with":[56,132],"temporal":[57,130],"trends.":[58,159],"Estimating":[59],"node":[62],"is":[64,108],"crucial":[65],"city-wide":[67],"spatio-temporal":[68,97,113,155,174],"sensing":[69],"urban":[71,101],"development,":[72],"extending":[73],"beyond":[74],"simple":[75],"point":[76,88],"block":[78],"imputation.":[80],"In":[81],"this":[82],"study,":[83],"we":[84,167],"introduce":[85],"diffusion":[87,98,126,146,170],"process":[89],"high-frequency":[91],"space":[92],"develop":[94],"robust":[96],"transformer":[99,134,150],"where":[103],"partial":[104],"reference":[106],"lacking.":[109],"Our":[110],"approach":[111],"decomposes":[112],"into":[115],"high":[116,138],"low-frequency":[118,143],"components":[119],"wavelet":[121],"transform,":[122],"trains":[124],"model":[127,171],"spatial":[129],"that":[135,177,196],"operates":[136],"on":[137],"frequency":[139],"signals.":[140],"We":[141],"incorporate":[142],"signals":[144],"as":[145],"conditions":[147],"architecture":[151],"capture":[153],"overall":[154],"profiles":[156],"gradual":[158],"To":[160],"enhance":[161],"learning":[163],"each":[165],"step,":[166],"design":[168],"an":[169],"featuring":[172],"attention":[175],"module":[176],"adaptively":[178],"captures":[179],"interdependencies":[180],"between":[181],"time":[182],"space.":[184],"Extensive":[185],"experiments":[186],"across":[187],"diverse":[188],"domains":[189],"including":[190],"traffic,":[191],"economics,":[192],"environment":[194],"demonstrate":[195],"our":[197],"method":[198],"significantly":[199],"outperforms":[200],"state-of-the-art":[201],"baselines.":[202]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-03-18T00:00:00"}