{"id":"https://openalex.org/W4387075204","doi":"https://doi.org/10.1145/3583780.3615487","title":"Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data","display_name":"Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data","publication_year":2023,"publication_date":"2023-10-21","ids":{"openalex":"https://openalex.org/W4387075204","doi":"https://doi.org/10.1145/3583780.3615487"},"language":"en","primary_location":{"id":"doi:10.1145/3583780.3615487","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2309.13452","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100619470","display_name":"Zhichao Chen","orcid":"https://orcid.org/0000-0001-5785-0741"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhichao Chen","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082406073","display_name":"L. K. Ding","orcid":"https://orcid.org/0000-0003-0641-3423"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leilei Ding","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008967163","display_name":"Zhixuan Chu","orcid":"https://orcid.org/0000-0001-6075-1816"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhixuan Chu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011307677","display_name":"Yucheng Qi","orcid":"https://orcid.org/0000-0003-4542-5427"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yucheng Qi","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018466261","display_name":"Jianmin Huang","orcid":"https://orcid.org/0000-0002-1861-7442"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianmin Huang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100704211","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3243-487X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Wang","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100619470"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.7817,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.93515096,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4523","last_page":"4529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9990000128746033,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9972000122070312,"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/T10320","display_name":"Neural Networks and Applications","score":0.9929999709129333,"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/monotonic-function","display_name":"Monotonic function","score":0.8357536792755127},{"id":"https://openalex.org/keywords/ordinary-differential-equation","display_name":"Ordinary differential equation","score":0.6343340873718262},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6122562289237976},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6061387658119202},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5658353567123413},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5413063168525696},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5232966542243958},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.4762953817844391},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4084106385707855},{"id":"https://openalex.org/keywords/differential-equation","display_name":"Differential equation","score":0.3767138123512268},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.35813841223716736},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3043139576911926},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2919354736804962},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24724584817886353},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09723716974258423}],"concepts":[{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.8357536792755127},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.6343340873718262},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122562289237976},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6061387658119202},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5658353567123413},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5413063168525696},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5232966542243958},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.4762953817844391},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4084106385707855},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.3767138123512268},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.35813841223716736},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3043139576911926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2919354736804962},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24724584817886353},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09723716974258423},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3583780.3615487","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583780.3615487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2309.13452","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.13452","pdf_url":"https://arxiv.org/pdf/2309.13452","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":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2309.13452","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.13452","pdf_url":"https://arxiv.org/pdf/2309.13452","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":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4387075204.pdf"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1543750907","https://openalex.org/W1924770834","https://openalex.org/W2130942839","https://openalex.org/W2747599906","https://openalex.org/W2792764867","https://openalex.org/W2954731415","https://openalex.org/W2963755523","https://openalex.org/W2964010366","https://openalex.org/W2971278153","https://openalex.org/W2999905431","https://openalex.org/W3025713978","https://openalex.org/W3042623101","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3212890323","https://openalex.org/W4205252517","https://openalex.org/W4225494949","https://openalex.org/W4296154082","https://openalex.org/W4312359551","https://openalex.org/W4385245566","https://openalex.org/W4385763767"],"related_works":["https://openalex.org/W2077314575","https://openalex.org/W4315701745","https://openalex.org/W1990290471","https://openalex.org/W4380682190","https://openalex.org/W2005710836","https://openalex.org/W2102386043","https://openalex.org/W2945307361","https://openalex.org/W2116636209","https://openalex.org/W2040930611","https://openalex.org/W2043671984"],"abstract_inverted_index":{"Time-Series":[0],"Forecasting":[1],"based":[2],"on":[3],"Cumulative":[4],"Data":[5],"(TSFCD)":[6],"is":[7],"a":[8,45,87],"crucial":[9],"problem":[10],"in":[11,78,86,107],"decision-making":[12],"across":[13],"various":[14],"industrial":[15],"scenarios.":[16],"However,":[17],"existing":[18],"time-series":[19],"forecasting":[20,113],"methods":[21],"often":[22],"overlook":[23],"two":[24],"important":[25],"characteristics":[26],"of":[27,58],"cumulative":[28,80,108],"data,":[29],"namely":[30],"monotonicity":[31,75,104],"and":[32,72,76,105,110],"irregularity,":[33],"which":[34],"limit":[35],"their":[36],"practical":[37,79],"applicability.":[38],"To":[39],"address":[40],"this":[41],"limitation,":[42],"we":[43,66,91],"propose":[44],"principled":[46],"approach":[47],"called":[48],"Monotonic":[49],"neural":[50,59],"Ordinary":[51],"Differential":[52],"Equation":[53],"(MODE)":[54],"within":[55],"the":[56,74],"framework":[57],"ordinary":[60],"differential":[61],"equations.":[62],"By":[63],"leveraging":[64],"MODE,":[65],"are":[67],"able":[68],"to":[69,101],"effectively":[70],"capture":[71],"represent":[73],"irregularity":[77,106],"data.":[81],"Through":[82],"extensive":[83],"experiments":[84],"conducted":[85],"bonus":[88],"allocation":[89],"scenario,":[90],"demonstrate":[92],"that":[93],"MODE":[94],"outperforms":[95],"state-of-the-art":[96],"methods,":[97],"showcasing":[98],"its":[99],"ability":[100],"handle":[102],"both":[103],"data":[109],"delivering":[111],"superior":[112],"performance.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2023-09-27T00:00:00"}
