{"id":"https://openalex.org/W7164829406","doi":"https://doi.org/10.48550/arxiv.2606.14604","title":"A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health","display_name":"A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164829406","doi":"https://doi.org/10.48550/arxiv.2606.14604"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14604","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,"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.2606.14604","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071069290","display_name":"Pavlos Nicolaou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolaou, Pavlos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068714009","display_name":"Kleanthis Malialis","orcid":"https://orcid.org/0000-0003-3432-7434"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Malialis, Kleanthis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138636867","display_name":"Artemis Kontou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kontou, Artemis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138640696","display_name":"Panayiotis Kolios","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kolios, Panayiotis","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/T11519","display_name":"Digital Mental Health Interventions","score":0.7305999994277954,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11519","display_name":"Digital Mental Health Interventions","score":0.7305999994277954,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.0414000004529953,"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/T11446","display_name":"Mobile Health and mHealth Applications","score":0.01730000041425228,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7369999885559082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7261000275611877},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6592000126838684},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.579200029373169},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.4846999943256378},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4830000102519989},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.4212999939918518},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.40630000829696655}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7369999885559082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7261000275611877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897000074386597},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6592000126838684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6484000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6019999980926514},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.4846999943256378},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.40630000829696655},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34940001368522644},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C60952562","wikidata":"https://www.wikidata.org/wiki/Q6887246","display_name":"Mobile technology","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2678999900817871},{"id":"https://openalex.org/C2775841894","wikidata":"https://www.wikidata.org/wiki/Q4683692","display_name":"Sleep (system call)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.26109999418258667},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14604","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,"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.2606.14604","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14604","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,"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Wearable":[0],"devices":[1],"and":[2,44,63,82,100,107,150,163,177,202],"smartphones":[3],"generate":[4],"rich":[5],"behavioural":[6,206],"time":[7,81],"series":[8],"that":[9],"can":[10],"support":[11],"proactive":[12],"health":[13,182],"interventions,":[14],"yet":[15],"systematic":[16],"comparisons":[17],"of":[18,187],"modern":[19,198],"forecasting":[20,46,207],"architectures":[21,39,99],"for":[22,77,180,204],"these":[23],"data":[24],"are":[25],"lacking.":[26],"In":[27],"particular,":[28],"it":[29],"remains":[30],"unclear":[31],"how":[32,37,45],"models":[33,123,144],"generalise":[34],"across":[35,49,85,96,104],"populations,":[36],"different":[38],"respond":[40],"to":[41,195],"participant-level":[42,152],"fine-tuning":[43,153],"accuracy":[47],"degrades":[48],"multi-day":[50],"horizons.":[51,88],"We":[52,89],"benchmark":[53],"six":[54,98],"deep":[55,199],"learning":[56],"architectures,":[57],"two":[58],"zero-shot":[59],"Foundation":[60],"Models":[61],"(FM)":[62],"statistical":[64],"baselines":[65],"on":[66,172],"three":[67,126],"public":[68],"datasets":[69],"encompassing":[70],"over":[71],"800":[72],"participants,":[73],"reporting":[74],"per-feature":[75,93,155],"metrics":[76],"step":[78,164],"counts,":[79],"screen":[80],"sleep":[83,160],"duration":[84],"1-8":[86],"day":[87],"further":[90],"conduct":[91],"a":[92],"personalisation":[94,178,203],"study":[95,194],"all":[97],"assess":[101],"FM":[102,138,175],"transferability":[103],"dataset":[105],"sizes":[106],"temporal":[108],"granularities.":[109],"Our":[110],"key":[111],"findings":[112],"are:":[113],"(i)":[114],"no":[115,132],"single":[116],"architecture":[117,173],"dominates,":[118],"PatchTST":[119],"leads":[120],"among":[121],"trained":[122,143],"while":[124],"the":[125,137,185,192],"runners-up":[127],"(TCN,":[128],"MLP,":[129],"Transformer)":[130],"show":[131],"meaningful":[133],"performance":[134],"difference;":[135],"(ii)":[136],"TimesFM":[139],"matches":[140],"or":[141],"exceeds":[142],"zero-shot,":[145],"especially":[146],"in":[147],"low-data":[148],"regimes":[149],"(iii)":[151],"reduces":[154],"RMSE":[156],"by":[157],"16-60\\%,":[158],"with":[159],"benefiting":[161],"most":[162],"counts":[165],"least.":[166],"These":[167],"results":[168],"provide":[169],"practical":[170],"guidance":[171],"selection,":[174],"applicability":[176],"strategies":[179],"mobile":[181],"forecasting.":[183],"To":[184],"best":[186],"our":[188],"knowledge,":[189],"this":[190],"is":[191],"first":[193],"jointly":[196],"evaluate":[197],"learning,":[200],"FMs":[201],"multi-horizon":[205],"from":[208],"wearables.":[209]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-16T00:00:00"}