{"id":"https://openalex.org/W4226164854","doi":"https://doi.org/10.48550/arxiv.2203.03328","title":"Automated Few-Shot Time Series Forecasting based on Bi-level Programming","display_name":"Automated Few-Shot Time Series Forecasting based on Bi-level Programming","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226164854","doi":"https://doi.org/10.48550/arxiv.2203.03328"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2203.03328","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2203.03328","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109544269","display_name":"Jiangjiao Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiangjiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100343450","display_name":"Ke Li","orcid":"https://orcid.org/0000-0001-7200-4244"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ke","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"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":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":60},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995,"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":0.9995,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9888,"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/T12676","display_name":"Machine Learning and ELM","score":0.9829,"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/hyperparameter","display_name":"Hyperparameter","score":0.7387004},{"id":"https://openalex.org/keywords/hyperparameter-optimization","display_name":"Hyperparameter optimization","score":0.62169206},{"id":"https://openalex.org/keywords/load-forecasting","display_name":"Load Forecasting","score":0.613241},{"id":"https://openalex.org/keywords/short-term-forecasting","display_name":"Short-Term Forecasting","score":0.57346},{"id":"https://openalex.org/keywords/forecasting","display_name":"Forecasting","score":0.562024},{"id":"https://openalex.org/keywords/electricity-price-forecasting","display_name":"Electricity Price Forecasting","score":0.516579},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic Forecasting","score":0.503823}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8009573},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7387004},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.689136},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.62169206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4989698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4603884},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46004987},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.43187737},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.410787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33846438},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.09615865},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.085776776},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2203.03328","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2203.03328","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2203.03328","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":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.84,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4388119537","https://openalex.org/W4287818966","https://openalex.org/W4281646320","https://openalex.org/W4205712847","https://openalex.org/W3200811867","https://openalex.org/W3192751261","https://openalex.org/W3169687406","https://openalex.org/W3114025147","https://openalex.org/W3014750173","https://openalex.org/W2953665647"],"abstract_inverted_index":{"New":[0],"micro-grid":[1],"design":[2,110],"with":[3],"renewable":[4,63],"energy":[5,30,64,197],"sources":[6],"and":[7,17,32,53,153],"battery":[8],"storage":[9],"systems":[10],"can":[11,169],"help":[12],"improve":[13],"greenhouse":[14],"gas":[15],"emissions":[16],"reduce":[18],"the":[19,43,48,57,67,79,89,108,122,127,131,136,140,146,157,180],"operational":[20],"cost.":[21],"To":[22],"provide":[23],"an":[24,74],"effective":[25],"short-/long-term":[26],"forecasting":[27,65],"of":[28,42,56,60,69,81,91,111,182],"both":[29,151],"generation":[31],"load":[33],"demand,":[34],"time":[35,61],"series":[36,62],"predictive":[37,76],"modeling":[38],"has":[39],"been":[40],"one":[41],"key":[44],"tools":[45],"to":[46,72,88,129,187],"guide":[47],"optimal":[49,109,147],"decision-making":[50],"for":[51,145,150,189,195],"planning":[52],"operation.":[54],"One":[55],"critical":[58],"challenges":[59],"is":[66,86,160],"lack":[68],"historical":[70],"data":[71,133],"train":[73],"adequate":[75],"model.":[77],"Moreover,":[78],"performance":[80],"a":[82,103,112,117,173,190],"machine":[83,166],"learning":[84,114,167,193],"model":[85],"sensitive":[87],"choice":[90],"its":[92],"corresponding":[93],"hyperparameters.":[94],"Bearing":[95],"these":[96],"considerations":[97],"in":[98,172],"mind,":[99],"this":[100],"paper":[101],"develops":[102],"BiLO-Auto-TSF/ML":[104,185],"framework":[105,159,186],"that":[106,156,163],"automates":[107],"few-shot":[113,192],"pipeline":[115,194],"from":[116],"bi-level":[118],"programming":[119],"perspective.":[120],"Specifically,":[121],"lower-level":[123],"meta-learning":[124],"helps":[125],"boost":[126],"base-learner":[128],"mitigate":[130],"small":[132],"challenge":[134],"while":[135],"hyperparameter":[137,148],"optimization":[138],"at":[139],"upper":[141],"level":[142],"proactively":[143],"searches":[144],"configurations":[149],"base-":[152],"meta-learners.":[154],"Note":[155],"proposed":[158,184],"so":[161],"general":[162],"any":[164],"off-the-shelf":[165],"method":[168],"be":[170],"used":[171],"plug-in":[174],"manner.":[175],"Comprehensive":[176],"experiments":[177],"fully":[178],"demonstrate":[179],"effectiveness":[181],"our":[183],"search":[188],"high-performance":[191],"various":[196],"sources.":[198]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4226164854","counts_by_year":[],"updated_date":"2024-12-05T20:50:43.049644","created_date":"2022-05-05"}