{"id":"https://openalex.org/W4312912076","doi":"https://doi.org/10.1145/3549206.3549285","title":"Mid Price Prediction via Statistical Feature Expansion and Kernel Adaptive Filtering","display_name":"Mid Price Prediction via Statistical Feature Expansion and Kernel Adaptive Filtering","publication_year":2022,"publication_date":"2022-08-04","ids":{"openalex":"https://openalex.org/W4312912076","doi":"https://doi.org/10.1145/3549206.3549285"},"language":"en","primary_location":{"id":"doi:10.1145/3549206.3549285","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3549206.3549285","pdf_url":null,"source":{"id":"https://openalex.org/S4363609025","display_name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","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/A5005881059","display_name":"Shambhavi Mishra","orcid":"https://orcid.org/0000-0002-9593-4820"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shambhavi Mishra","raw_affiliation_strings":["Bennett University, India"],"affiliations":[{"raw_affiliation_string":"Bennett University, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100758415","display_name":"Tanveer Ahmed","orcid":"https://orcid.org/0000-0003-3182-0952"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tanveer Ahmed","raw_affiliation_strings":["Bennett University, India"],"affiliations":[{"raw_affiliation_string":"Bennett University, India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000068719","display_name":"Vipul Kumar Mishra","orcid":"https://orcid.org/0000-0002-3649-1388"},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vipul Mishra","raw_affiliation_strings":["Bennett University, India"],"affiliations":[{"raw_affiliation_string":"Bennett University, India","institution_ids":["https://openalex.org/I3129773123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005881059"],"corresponding_institution_ids":["https://openalex.org/I3129773123"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25227638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"461","last_page":"466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9933000206947327,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9929999709129333,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7098534107208252},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5861358642578125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5046812295913696},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.502999484539032},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5009298324584961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49747923016548157},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47437793016433716},{"id":"https://openalex.org/keywords/chaotic","display_name":"Chaotic","score":0.46245741844177246},{"id":"https://openalex.org/keywords/stock-exchange","display_name":"Stock exchange","score":0.46217411756515503},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44580331444740295},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.42722728848457336},{"id":"https://openalex.org/keywords/adaptive-filter","display_name":"Adaptive filter","score":0.4241065979003906},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.27291324734687805},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24811923503875732},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11299484968185425},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.07524749636650085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7098534107208252},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5861358642578125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5046812295913696},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.502999484539032},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5009298324584961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49747923016548157},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47437793016433716},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.46245741844177246},{"id":"https://openalex.org/C200870193","wikidata":"https://www.wikidata.org/wiki/Q11691","display_name":"Stock exchange","level":2,"score":0.46217411756515503},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44580331444740295},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.42722728848457336},{"id":"https://openalex.org/C102248274","wikidata":"https://www.wikidata.org/wiki/Q168388","display_name":"Adaptive filter","level":2,"score":0.4241065979003906},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.27291324734687805},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24811923503875732},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11299484968185425},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.07524749636650085},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3549206.3549285","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3549206.3549285","pdf_url":null,"source":{"id":"https://openalex.org/S4363609025","display_name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W842959368","https://openalex.org/W1994233698","https://openalex.org/W2007358469","https://openalex.org/W2012412464","https://openalex.org/W2053565514","https://openalex.org/W2074581693","https://openalex.org/W2112966028","https://openalex.org/W2120315477","https://openalex.org/W2186575060","https://openalex.org/W2400770063","https://openalex.org/W2761330267","https://openalex.org/W2772397068","https://openalex.org/W2783595736","https://openalex.org/W2791025763","https://openalex.org/W2799635155","https://openalex.org/W2802314367","https://openalex.org/W2921364842","https://openalex.org/W2955529518","https://openalex.org/W3100210912","https://openalex.org/W3126271061","https://openalex.org/W4213113461","https://openalex.org/W6623528525"],"related_works":["https://openalex.org/W4398232961","https://openalex.org/W2004708907","https://openalex.org/W2377429601","https://openalex.org/W2386043911","https://openalex.org/W3171270255","https://openalex.org/W2368252804","https://openalex.org/W3003512042","https://openalex.org/W2379367191","https://openalex.org/W1981741235","https://openalex.org/W4205436246"],"abstract_inverted_index":{"Predicting":[0],"mid-price":[1,44,119],"movements":[2],"of":[3,28,40,56,71,84,93,113,127,164],"stocks":[4],"is":[5,10,20,65,106],"a":[6,16,25,34,62],"difficult":[7],"task.":[8],"It":[9],"mainly":[11],"owing":[12],"to":[13,60,79,154],"the":[14,38,54,82,85,96,111,122,149,165],"fact":[15],"financial":[17],"times":[18],"series":[19],"often":[21],"non-stationary,":[22],"chaotic,":[23],"and":[24,43,67,118,140,162,170],"dynamic":[26],"mixture":[27],"several":[29,91],"factors.":[30],"This":[31,151],"work":[32,89,152],"provides":[33],"thorough":[35],"investigation":[36],"on":[37,90,133],"use":[39,76],"statistical":[41],"features":[42,78],"prediction":[45,86,120],"via":[46],"kernel":[47],"adaptive":[48],"filtering":[49],"algorithms.":[50,128],"First,":[51],"we":[52,75],"apply":[53],"process":[55],"feature":[57,115,168],"expansion":[58],"(FE)":[59],"build":[61],"procedure":[63],"that":[64,142],"effective":[66],"efficient":[68],"in":[69,148],"terms":[70],"price":[72],"prediction.":[73],"Subsequently,":[74],"these":[77,143],"improve":[80],"upon":[81],"accuracy":[83],"model.":[87],"We":[88,129],"years":[92],"data":[94,171],"from":[95],"National":[97],"Stock":[98],"Exchange":[99],"(NSE-50)":[100],"for":[101],"experimentation.":[102],"The":[103],"proposed":[104],"solution":[105],"comprehensive":[107,131],"as":[108],"it":[109],"includes":[110],"utilization":[112],"multiple":[114],"engineering":[116],"techniques":[117,144],"using":[121],"Kernel":[123,136],"Adaptive":[124,137],"Filtering":[125,138],"class":[126],"conduct":[130],"studies":[132],"ten":[134],"different":[135],"algorithms":[139],"show":[141],"outperform":[145],"similar":[146],"methods":[147],"literature.":[150],"contributes":[153],"stock":[155],"analysis":[156],"research":[157],"by":[158],"providing":[159],"extensive":[160],"design":[161],"evaluation":[163],"predictive":[166],"capability,":[167],"engineering,":[169],"pre-processing":[172],"approaches.":[173]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}