{"id":"https://openalex.org/W4392014327","doi":"https://doi.org/10.3390/forecast6010010","title":"Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions","display_name":"Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions","publication_year":2024,"publication_date":"2024-02-16","ids":{"openalex":"https://openalex.org/W4392014327","doi":"https://doi.org/10.3390/forecast6010010"},"language":"en","primary_location":{"id":"doi:10.3390/forecast6010010","doi":"https://doi.org/10.3390/forecast6010010","is_oa":true,"landing_page_url":"https://doi.org/10.3390/forecast6010010","pdf_url":"https://www.mdpi.com/2571-9394/6/1/10/pdf?version=1708090563","source":{"id":"https://openalex.org/S4210194518","display_name":"Forecasting","issn_l":"2571-9394","issn":["2571-9394"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Forecasting","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2571-9394/6/1/10/pdf?version=1708090563","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092057091","display_name":"Konstantinos Panagiotis Fourkiotis","orcid":"https://orcid.org/0009-0000-8580-7812","observed_orcids":["https://orcid.org/0009-0000-8580-7812"]},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Konstantinos P. Fourkiotis","raw_affiliation_strings":["School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021086667","display_name":"Athanasios K. Tsadiras","orcid":"https://orcid.org/0000-0002-9887-8983","observed_orcids":["https://orcid.org/0000-0002-9887-8983"]},"institutions":[{"id":"https://openalex.org/I21370196","display_name":"Aristotle University of Thessaloniki","ror":"https://ror.org/02j61yw88","country_code":"GR","type":"education","lineage":["https://openalex.org/I21370196"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Athanasios Tsadiras","raw_affiliation_strings":["School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"],"raw_orcid":"https://orcid.org/0000-0002-9887-8983","affiliations":[{"raw_affiliation_string":"School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece","institution_ids":["https://openalex.org/I21370196"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5021086667"],"corresponding_institution_ids":["https://openalex.org/I21370196"],"apc_list":{"value":1800,"currency":"CHF","value_usd":2046},"apc_paid":{"value":1800,"currency":"CHF","value_usd":2046},"fwci":14.0563,"has_fulltext":true,"cited_by_count":56,"citation_normalized_percentile":{"value":0.99258632,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"6","issue":"1","first_page":"170","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.989300012588501,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.989300012588501,"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":0.005400000140070915,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.0034000000450760126,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/drug-demand-forecasting","display_name":"drug demand forecasting","score":0.9940000176429749},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.7820000052452087},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.6650000214576721},{"id":"https://openalex.org/keywords/arima","display_name":"ARIMA","score":0.9570000171661377},{"id":"https://openalex.org/keywords/atc-classification","display_name":"ATC classification","score":0.722000002861023},{"id":"https://openalex.org/keywords/seasonal-variation","display_name":"seasonal variation","score":0.7770000100135803},{"id":"https://openalex.org/keywords/pharmacy-sales-data","display_name":"pharmacy sales data","score":0.5889999866485596},{"id":"https://openalex.org/keywords/anti-inflammatory-agents","display_name":"anti-inflammatory agents","score":0.7310000061988831},{"id":"https://openalex.org/keywords/analgesics","display_name":"analgesics","score":0.8510000109672546},{"id":"https://openalex.org/keywords/sedative-hypnotics","display_name":"sedative-hypnotic drugs","score":0.9300000071525574},{"id":"https://openalex.org/keywords/forecasting-accuracy-comparison","display_name":"forecast accuracy comparison","score":0.5559999942779541},{"id":"https://openalex.org/keywords/univariate-forecasting","display_name":"univariate time series forecasting","score":0.6510000228881836},{"id":"https://openalex.org/keywords/simple-exponential-smoothing","display_name":"single exponential smoothing","score":0.7350000143051147},{"id":"https://openalex.org/keywords/seasonal-naive-forecast","display_name":"seasonal naive forecast","score":0.7940000295639038}],"concepts":[{"id":"https://openalex.org/C2984642479","wikidata":"https://www.wikidata.org/wiki/Q7404320","display_name":"Sales forecasting","level":2,"score":0.5727496147155762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5462746620178223},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.539649248123169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5152326226234436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5071496963500977},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29945918917655945},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17568883299827576}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/forecast6010010","doi":"https://doi.org/10.3390/forecast6010010","is_oa":true,"landing_page_url":"https://doi.org/10.3390/forecast6010010","pdf_url":"https://www.mdpi.com/2571-9394/6/1/10/pdf?version=1708090563","source":{"id":"https://openalex.org/S4210194518","display_name":"Forecasting","issn_l":"2571-9394","issn":["2571-9394"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Forecasting","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:gam:jforec:v:6:y:2024:i:1:p:10-186:d:1340047","doi":null,"is_oa":false,"landing_page_url":"https://www.mdpi.com/2571-9394/6/1/10/","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"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":"article"},{"id":"pmh:oai:doaj.org/article:cb9fa4b1c909406988ca7837e8fbd2e1","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/cb9fa4b1c909406988ca7837e8fbd2e1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Forecasting, Vol 6, Iss 1, Pp 170-186 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/forecast6010010","doi":"https://doi.org/10.3390/forecast6010010","is_oa":true,"landing_page_url":"https://doi.org/10.3390/forecast6010010","pdf_url":"https://www.mdpi.com/2571-9394/6/1/10/pdf?version=1708090563","source":{"id":"https://openalex.org/S4210194518","display_name":"Forecasting","issn_l":"2571-9394","issn":["2571-9394"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Forecasting","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8048999905586243,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392014327.pdf","grobid_xml":"https://content.openalex.org/works/W4392014327.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1501910625","https://openalex.org/W1969743139","https://openalex.org/W1991317100","https://openalex.org/W2017044837","https://openalex.org/W2081676608","https://openalex.org/W2111338617","https://openalex.org/W2128388077","https://openalex.org/W2139252701","https://openalex.org/W2295598076","https://openalex.org/W2297152540","https://openalex.org/W2523635132","https://openalex.org/W2801966202","https://openalex.org/W2900172858","https://openalex.org/W2908504721","https://openalex.org/W2921612017","https://openalex.org/W2954458386","https://openalex.org/W2994712268","https://openalex.org/W3021959513","https://openalex.org/W3135294663","https://openalex.org/W3138882923","https://openalex.org/W3143943806","https://openalex.org/W3152804902","https://openalex.org/W3158906450","https://openalex.org/W3214721780","https://openalex.org/W4200335566","https://openalex.org/W4206062825","https://openalex.org/W4213345203","https://openalex.org/W4282038787","https://openalex.org/W4284685691","https://openalex.org/W4302424782","https://openalex.org/W4310362879"],"related_works":["https://openalex.org/W4383747227","https://openalex.org/W4404241290","https://openalex.org/W7161830286","https://openalex.org/W4414464125","https://openalex.org/W4312976482","https://openalex.org/W7220175719","https://openalex.org/W4409274790","https://openalex.org/W4282038787","https://openalex.org/W7202277093","https://openalex.org/W7213288693"],"abstract_inverted_index":{"In":[0,311],"today\u2019s":[1],"evolving":[2],"global":[3,19],"world,":[4],"the":[5,14,18,22,34,64,84,136,155,167,180,189,200,252,270,282,297,308,316,332],"pharmaceutical":[6,39,85,328],"sector":[7],"faces":[8],"an":[9,275,288],"emerging":[10],"challenge,":[11],"which":[12],"is":[13],"rapid":[15],"surge":[16],"of":[17,67,109,138,170,182,192,277,290,305,318,334],"population":[20],"and":[21,46,53,75,147,208,215,222,226,235,237,244,262,267,344],"consequent":[23],"growth":[24],"in":[25,83],"drug":[26,68,173],"production":[27,40],"demands.":[28],"Recognizing":[29],"this,":[30],"our":[31,57,60,102,139,143,313],"study":[32,103],"explores":[33],"urgent":[35],"need":[36],"to":[37,49,80,323],"strengthen":[38],"capacities,":[41],"ensuring":[42],"drugs":[43],"are":[44],"allocated":[45],"stored":[47],"strategically":[48],"meet":[50],"diverse":[51,303],"regional":[52],"demographic":[54],"needs.":[55],"Summarizing":[56],"key":[58],"findings,":[59],"research":[61,314],"focuses":[62],"on":[63,105,154,172],"promising":[65],"area":[66],"demand":[69],"forecasting":[70,309],"using":[71],"artificial":[72],"intelligence":[73],"(AI)":[74],"machine":[76,183,320],"learning":[77,184,321],"(ML)":[78],"techniques":[79,322],"enhance":[81],"predictions":[82],"field.":[86],"Supplied":[87],"with":[88,123,131,251],"a":[89,99,106,132,302],"rich":[90],"dataset":[91],"from":[92,98,300],"Kaggle":[93],"spanning":[94],"600,000":[95],"sales":[96],"records":[97],"singular":[100,133],"pharmacy,":[101],"embarks":[104],"thorough":[107],"exploration":[108],"univariate":[110],"time":[111],"series":[112],"analysis.":[113],"Here,":[114],"we":[115],"pair":[116],"conventional":[117],"analytical":[118],"tools":[119],"such":[120],"as":[121],"ARIMA":[122],"advanced":[124],"methodologies":[125],"like":[126],"LSTM":[127],"neural":[128],"networks,":[129],"all":[130],"vision:":[134],"refining":[135],"precision":[137,250],"sales.":[140,174],"Venturing":[141],"deeper,":[142],"data":[144],"underwent":[145],"categorisation":[146],"were":[148],"segmented":[149],"into":[150],"eight":[151],"clusters":[152],"premised":[153],"ATC":[156],"Anatomical":[157],"Therapeutic":[158],"Chemical":[159],"(ATC)":[160],"Classification":[161],"System":[162],"framework.":[163],"This":[164,194],"segmentation":[165],"unravels":[166],"evident":[168],"influence":[169],"seasonality":[171],"The":[175],"analysis":[176],"not":[177],"only":[178],"highlights":[179],"effectiveness":[181],"models":[185],"but":[186],"also":[187],"illuminates":[188],"remarkable":[190],"success":[191],"XGBoost.":[193],"algorithm":[195],"outperformed":[196],"traditional":[197],"models,":[198],"achieving":[199],"lowest":[201,253],"MAPE":[202],"values:":[203],"17.89%":[204],"for":[205,219,231,239,257,260,264,279,292,327],"M01AB":[206],"(anti-inflammatory":[207,221],"antirheumatic":[209,223],"products,":[210,224],"non-steroids,":[211,225],"acetic":[212],"acid":[213,228],"derivatives,":[214],"related":[216],"substances),":[217],"16.92%":[218],"M01AE":[220],"propionic":[227],"derivatives),":[229],"17.98%":[230],"N02BA":[232],"(analgesics,":[233,241],"antipyretics,":[234,242],"anilides),":[236],"16.05%":[238],"N02BE":[240],"pyrazolones,":[243],"anilides).":[245],"XGBoost":[246],"further":[247],"demonstrated":[248],"exceptional":[249],"MSE":[254,276,289],"scores:":[255],"28.8":[256],"M01AB,":[258],"1518.56":[259],"N02BE,":[261],"350.84":[263],"N05C":[265],"(hypnotics":[266],"sedatives).":[268],"Additionally,":[269],"Seasonal":[271],"Na\u00efve":[272],"model":[273,286],"recorded":[274],"49.19":[278],"M01AE,":[280],"while":[281],"Single":[283],"Exponential":[284],"Smoothing":[285],"showed":[287],"7.19":[291],"N05B.":[293],"These":[294],"findings":[295],"underscore":[296],"strengths":[298],"derived":[299],"employing":[301],"range":[304],"approaches":[306],"within":[307],"series.":[310],"summary,":[312],"accentuates":[315],"significance":[317],"leveraging":[319],"derive":[324],"valuable":[325],"insights":[326],"companies.":[329],"By":[330],"applying":[331],"power":[333],"these":[335],"methods,":[336],"companies":[337],"can":[338],"optimize":[339],"their":[340],"production,":[341],"storage,":[342],"distribution,":[343],"marketing":[345],"practices.":[346]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":8}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-02-22T00:00:00"}
