{"id":"https://openalex.org/W2912050782","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633040","title":"Particle Filter Based Time Series Prediction of Daily Sales of an Online Retailer","display_name":"Particle Filter Based Time Series Prediction of Daily Sales of an Online Retailer","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2912050782","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633040","mag":"2912050782"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2018.8633040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5076160218","display_name":"Xueye Ping","orcid":null},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xueye Ping","raw_affiliation_strings":["Mathematical Sciences, Xi an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003063016","display_name":"Qinyi Chen","orcid":"https://orcid.org/0000-0002-2912-2728"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinyi Chen","raw_affiliation_strings":["Mathematical Sciences, Xi an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Mathematical Sciences, Xi an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101823656","display_name":"Guoquan Liu","orcid":"https://orcid.org/0000-0001-6416-5398"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoquan Liu","raw_affiliation_strings":["International Business School Suzhou, Xian Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"International Business School Suzhou, Xian Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085156744","display_name":"Jionglong Su","orcid":"https://orcid.org/0000-0001-5360-6493"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jionglong Su","raw_affiliation_strings":["Research Center for Precision Medicine, HT-URC, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Research Center for Precision Medicine, HT-URC, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062318925","display_name":"Fei Ma","orcid":"https://orcid.org/0000-0001-6099-480X"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Ma","raw_affiliation_strings":["Research Center for Precision Medicine, HT-URC, Xi'an Jiaotong-Liverpool University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Research Center for Precision Medicine, HT-URC, Xi'an Jiaotong-Liverpool University, Suzhou, China","institution_ids":["https://openalex.org/I69356397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076160218"],"corresponding_institution_ids":["https://openalex.org/I69356397"],"apc_list":null,"apc_paid":null,"fwci":0.8257,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74812703,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9988999962806702,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9927999973297119,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9919000267982483,"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/resampling","display_name":"Resampling","score":0.7210336327552795},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7000061869621277},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.666350245475769},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6544805765151978},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6131229996681213},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6113482117652893},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5959045886993408},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5792572498321533},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.38499119877815247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27734702825546265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21978625655174255},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1858585774898529},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.13310259580612183},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.11611664295196533}],"concepts":[{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7210336327552795},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7000061869621277},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.666350245475769},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6544805765151978},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6131229996681213},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6113482117652893},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5959045886993408},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5792572498321533},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38499119877815247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27734702825546265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21978625655174255},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1858585774898529},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.13310259580612183},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.11611664295196533},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2018.8633040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633040","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W645532996","https://openalex.org/W1513008779","https://openalex.org/W1522700451","https://openalex.org/W1575020748","https://openalex.org/W1910978492","https://openalex.org/W1981903823","https://openalex.org/W2064480843","https://openalex.org/W2082542916","https://openalex.org/W2098613108","https://openalex.org/W2105119576","https://openalex.org/W2117014758","https://openalex.org/W2126736494","https://openalex.org/W2137864425","https://openalex.org/W2139320579","https://openalex.org/W2140971281","https://openalex.org/W2160337655","https://openalex.org/W2353311365","https://openalex.org/W2369427069","https://openalex.org/W2507089454","https://openalex.org/W2526137910","https://openalex.org/W2531982381","https://openalex.org/W2747422785","https://openalex.org/W2780739533","https://openalex.org/W2786270406","https://openalex.org/W2791754744","https://openalex.org/W2808519342","https://openalex.org/W2808739674","https://openalex.org/W2810279840","https://openalex.org/W2888659012","https://openalex.org/W3143839440","https://openalex.org/W4230472026","https://openalex.org/W4249198916","https://openalex.org/W4292963524","https://openalex.org/W4386546493"],"related_works":["https://openalex.org/W2927378857","https://openalex.org/W2801696468","https://openalex.org/W2406829934","https://openalex.org/W1824810860","https://openalex.org/W2185006999","https://openalex.org/W1583020711","https://openalex.org/W2138381686","https://openalex.org/W2162253570","https://openalex.org/W2010934810","https://openalex.org/W3144709167"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,74],"of":[2,48,100,105],"sales":[3,47,101],"is":[4,13,43],"instrumental":[5],"to":[6],"successful":[7],"management":[8],"in":[9,15,28,51],"the":[10,85,94,106,112,122],"industries.":[11],"It":[12],"crucial":[14],"formulating":[16],"business":[17],"strategies":[18],"under":[19],"uncertainties.":[20],"In":[21],"this":[22],"paper,":[23],"we":[24],"consider":[25],"time":[26,36,72],"series":[27,37,73],"which":[29],"observations":[30],"are":[31],"arriving":[32],"sequentially.":[33],"An":[34],"online":[35,54],"model":[38,63,68,96,118],"integrating":[39],"with":[40],"particle":[41],"filter":[42],"used":[44],"for":[45,75,121],"predicting":[46],"80":[49,77],"products":[50,78],"a":[52,65,80],"local":[53],"retailer":[55],"over":[56],"400":[57],"days.":[58],"We":[59],"embed":[60],"an":[61],"Autoregressive":[62,114],"into":[64],"state":[66],"space":[67],"and":[69],"carry":[70],"out":[71],"all":[76],"using":[79],"particular":[81],"Particle":[82],"Filter":[83],"called":[84],"Sampling":[86],"Importance":[87],"Resampling":[88],"Filter.":[89],"Our":[90],"experiment":[91],"shows":[92],"that":[93],"proposed":[95],"successfully":[97],"predicts":[98],"27.5%":[99],"fluctuating":[102],"within":[103],"10%":[104],"true":[107],"values.":[108],"Furthermore,":[109],"it":[110],"outperforms":[111],"traditional":[113],"Integrated":[115],"Moving":[116],"Average":[117],"by":[119],"5%":[120],"same":[123],"metric":[124],"used.":[125]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}