{"id":"https://openalex.org/W4312775161","doi":"https://doi.org/10.1109/idsta55301.2022.9923044","title":"Discrete Sequencing for Demand Forecasting: A novel data sampling technique for time series forecasting","display_name":"Discrete Sequencing for Demand Forecasting: A novel data sampling technique for time series forecasting","publication_year":2022,"publication_date":"2022-09-05","ids":{"openalex":"https://openalex.org/W4312775161","doi":"https://doi.org/10.1109/idsta55301.2022.9923044"},"language":"en","primary_location":{"id":"doi:10.1109/idsta55301.2022.9923044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idsta55301.2022.9923044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035087227","display_name":"Nisha Menon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nisha Menon","raw_affiliation_strings":["T DAI ADM-IN Siemens Research,Bengaluru,India","T DAI ADM-IN Siemens Research, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"T DAI ADM-IN Siemens Research,Bengaluru,India","institution_ids":["https://openalex.org/I4210151799"]},{"raw_affiliation_string":"T DAI ADM-IN Siemens Research, Bengaluru, India","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068726044","display_name":"Shantanu Saboo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shantanu Saboo","raw_affiliation_strings":["T DAI ADM-IN Siemens Research,Bengaluru,India","T DAI ADM-IN Siemens Research, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"T DAI ADM-IN Siemens Research,Bengaluru,India","institution_ids":["https://openalex.org/I4210151799"]},{"raw_affiliation_string":"T DAI ADM-IN Siemens Research, Bengaluru, India","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032694412","display_name":"Tanmay Ambadkar","orcid":"https://orcid.org/0000-0003-0544-6864"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tanmay Ambadkar","raw_affiliation_strings":["T DAI ADM-IN Siemens Research,Bengaluru,India","T DAI ADM-IN Siemens Research, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"T DAI ADM-IN Siemens Research,Bengaluru,India","institution_ids":["https://openalex.org/I4210151799"]},{"raw_affiliation_string":"T DAI ADM-IN Siemens Research, Bengaluru, India","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084857972","display_name":"Umesh Uppili","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Umesh Uppili","raw_affiliation_strings":["T DAI ADM-IN Siemens Research,Bengaluru,India","T DAI ADM-IN Siemens Research, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"T DAI ADM-IN Siemens Research,Bengaluru,India","institution_ids":["https://openalex.org/I4210151799"]},{"raw_affiliation_string":"T DAI ADM-IN Siemens Research, Bengaluru, India","institution_ids":["https://openalex.org/I4210151799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035087227"],"corresponding_institution_ids":["https://openalex.org/I4210151799"],"apc_list":null,"apc_paid":null,"fwci":0.0915,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42347149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"67"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9818999767303467,"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":0.9671000242233276,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6482556462287903},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6466489434242249},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5961369872093201},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5755148530006409},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.5672154426574707},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5306032299995422},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5178682804107666},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.506494402885437},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4694962799549103},{"id":"https://openalex.org/keywords/horizon","display_name":"Horizon","score":0.4328128695487976},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3534281849861145},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.2956934869289398},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.23405015468597412},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23243993520736694},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21577084064483643},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.17045298218727112},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15605202317237854},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10881951451301575},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08583444356918335}],"concepts":[{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6482556462287903},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466489434242249},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5961369872093201},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5755148530006409},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.5672154426574707},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5306032299995422},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5178682804107666},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.506494402885437},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4694962799549103},{"id":"https://openalex.org/C159176650","wikidata":"https://www.wikidata.org/wiki/Q43261","display_name":"Horizon","level":2,"score":0.4328128695487976},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3534281849861145},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2956934869289398},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.23405015468597412},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23243993520736694},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21577084064483643},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.17045298218727112},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15605202317237854},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10881951451301575},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08583444356918335},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/idsta55301.2022.9923044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/idsta55301.2022.9923044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2906267174","https://openalex.org/W134501823","https://openalex.org/W2615656344","https://openalex.org/W2013239059","https://openalex.org/W2295626654","https://openalex.org/W4286622840","https://openalex.org/W2536121351","https://openalex.org/W2809608782","https://openalex.org/W2333245620","https://openalex.org/W1889576908"],"abstract_inverted_index":{"Accurately":[0],"forecasting":[1,101],"energy":[2,52,67,83],"consumption":[3],"for":[4,77,102,152],"buildings":[5],"has":[6],"become":[7,171],"increasingly":[8],"important":[9,60],"over":[10],"the":[11,15,29,34,39,55,63,82,119,124,132,143,156,209,217],"years":[12],"owing":[13],"to":[14,62,123,150],"increasing":[16],"prices":[17],"of":[18,26,33,51,66,110,161,211],"energy.":[19],"A":[20],"good":[21],"forecast":[22,151],"gives":[23],"an":[24,70,107],"understanding":[25],"how":[27],"much":[28,198],"expected":[30],"load":[31],"(demand)":[32],"building":[35],"would":[36],"be":[37,46],"in":[38,48,91,190],"coming":[40],"days":[41],"and":[42,86,98,127,170],"months.":[43],"This":[44,57,186],"could":[45,74],"used":[47],"further":[49],"planning":[50],"usage":[53],"within":[54],"building.":[56],"also":[58,75],"becomes":[59],"due":[61],"dynamic":[64],"nature":[65],"rates.":[68],"With":[69],"accurate":[71],"forecast,":[72],"one":[73,139],"aim":[76],"spot":[78],"trading":[79],"by":[80,215],"which":[81,194],"is":[84,106,134,148],"bought":[85],"sold":[87],"at":[88,196],"different":[89,221],"rates":[90],"a":[92,178,191,197,203],"daily":[93],"fashion.":[94],"We":[95,207],"target":[96],"short-term":[97,153],"medium-term":[99],"demand":[100],"buildings.":[103],"Data":[104],"Sampling":[105],"integral":[108],"part":[109],"training":[111],"time-series":[112],"models.":[113],"The":[114,165],"temporal":[115,200],"horizon":[116,201],"along":[117],"with":[118,136,202],"patterns":[120],"captured":[121],"contribute":[122],"model":[125],"learning":[126],"thus":[128],"its":[129,162],"forecasts.":[130],"When":[131],"data":[133,180,188],"aplenty":[135],"more":[137],"than":[138],"value":[140],"per":[141],"day,":[142],"traditional":[144],"sliding":[145],"window":[146],"method":[147],"unable":[149],"forecasts":[154,166,218],"without":[155],"actual":[157],"truth":[158],"values":[159],"because":[160],"continuous":[163],"nature.":[164],"deviate":[167],"very":[168],"quickly":[169],"unusable.":[172],"In":[173],"this":[174],"paper,":[175],"we":[176],"present":[177],"novel":[179],"sampling":[181,213],"technique":[182,214],"called":[183],"Discrete":[184],"Sequencing.":[185],"samples":[187],"sequences":[189],"lagged":[192],"fashion":[193],"looks":[195],"larger":[199],"smaller":[204],"sequence":[205],"size.":[206],"demonstrate":[208],"efficacy":[210],"our":[212],"testing":[216],"on":[219],"three":[220],"neural":[222],"network":[223],"architectures.":[224]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}