{"id":"https://openalex.org/W4389910829","doi":"https://doi.org/10.48550/arxiv.2312.09513","title":"CGS-Mask: Making Time Series Predictions Intuitive for All","display_name":"CGS-Mask: Making Time Series Predictions Intuitive for All","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4389910829","doi":"https://doi.org/10.48550/arxiv.2312.09513"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2312.09513","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.09513","pdf_url":"https://arxiv.org/pdf/2312.09513","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2312.09513","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100678072","display_name":"Feng Lu","orcid":"https://orcid.org/0000-0001-9349-1758"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lu, Feng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100318462","display_name":"Wei Li","orcid":"https://orcid.org/0000-0003-4731-3226"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103249572","display_name":"Yifei Sun","orcid":"https://orcid.org/0009-0005-7811-9487"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yifei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101763789","display_name":"Cheng Song","orcid":"https://orcid.org/0000-0003-2221-7772"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Cheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114705946","display_name":"Yufei Ren","orcid":"https://orcid.org/0000-0003-3749-8036"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Yufei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015993565","display_name":"Albert Y. Zomaya","orcid":"https://orcid.org/0000-0002-3090-1059"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zomaya, Albert Y.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100678072"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9663000106811523,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9648000001907349,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9542999863624573,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/computer-science","display_name":"Computer science","score":0.7801321744918823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5905070900917053},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5604687929153442},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5210002064704895},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5122898817062378},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4775068163871765},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.45302435755729675},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4347935914993286},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.4342457354068756},{"id":"https://openalex.org/keywords/time-point","display_name":"Time point","score":0.43342822790145874},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4309430420398712},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4148057997226715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7801321744918823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5905070900917053},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5604687929153442},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5210002064704895},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5122898817062378},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4775068163871765},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45302435755729675},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4347935914993286},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.4342457354068756},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.43342822790145874},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4309430420398712},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4148057997226715},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C107038049","wikidata":"https://www.wikidata.org/wiki/Q35986","display_name":"Aesthetics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2312.09513","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.09513","pdf_url":"https://arxiv.org/pdf/2312.09513","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2312.09513","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2312.09513","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2312.09513","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.09513","pdf_url":"https://arxiv.org/pdf/2312.09513","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4389910829.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W2977677679","https://openalex.org/W1992327129","https://openalex.org/W2381986121","https://openalex.org/W2370918718","https://openalex.org/W2256933480","https://openalex.org/W1993278628","https://openalex.org/W2027854990","https://openalex.org/W2370081953","https://openalex.org/W1535949710"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"(AI)":[2],"has":[3],"immense":[4],"potential":[5],"in":[6,17,130,147,171],"time":[7,35,39,49,93,175],"series":[8,50,176],"prediction,":[9,108],"but":[10],"most":[11,168],"explainable":[12],"tools":[13,28],"have":[14],"limited":[15],"capabilities":[16],"providing":[18,109],"a":[19,33,76,96,131,162],"systematic":[20],"understanding":[21],"of":[22,41,48,103,151,186],"important":[23],"features":[24,104,152],"over":[25,116,153],"time.":[26,117,133,154],"These":[27,52],"typically":[29],"rely":[30],"on":[31,105,137],"evaluating":[32],"single":[34],"point,":[36],"overlook":[37],"the":[38,45,101,106,121,127,149,167,183],"ordering":[40],"inputs,":[42],"and":[43,69,78,111,139,142],"neglect":[44],"time-sensitive":[46],"nature":[47],"applications.":[51],"factors":[53],"make":[54],"it":[55,143],"difficult":[56],"for":[57],"users,":[58],"particularly":[59],"those":[60],"without":[61],"domain":[62],"knowledge,":[63],"to":[64,86,99,125,156,181],"comprehend":[65,182],"AI":[66,187],"model":[67],"decisions":[68],"obtain":[70,126],"meaningful":[71],"explanations.":[72],"We":[73,134],"propose":[74],"CGS-Mask,":[75],"post-hoc":[77],"model-agnostic":[79],"cellular":[80],"genetic":[81],"strip":[82],"mask-based":[83],"saliency":[84],"approach":[85,170],"address":[87],"these":[88],"challenges.":[89],"CGS-Mask":[90,136,165],"uses":[91],"consecutive":[92],"steps":[94],"as":[95],"cohesive":[97],"entity":[98],"evaluate":[100],"impact":[102],"final":[107],"binary":[110],"sustained":[112],"feature":[113],"importance":[114,150],"scores":[115],"Our":[118],"algorithm":[119],"optimizes":[120],"mask":[122,129],"population":[123],"iteratively":[124],"optimal":[128],"reasonable":[132],"evaluated":[135],"synthetic":[138],"real-world":[140],"datasets,":[141],"outperformed":[144],"state-of-the-art":[145],"methods":[146],"elucidating":[148],"According":[155],"our":[157],"pilot":[158],"user":[159],"study":[160],"via":[161],"questionnaire":[163],"survey,":[164],"is":[166],"effective":[169],"presenting":[172],"easily":[173],"understandable":[174],"prediction":[177],"results,":[178],"enabling":[179],"users":[180],"decision-making":[184],"process":[185],"models":[188],"with":[189],"ease.":[190]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}