001443351 000__ 03623cam\a2200613Ia\4500
001443351 001__ 1443351
001443351 003__ OCoLC
001443351 005__ 20230310003540.0
001443351 006__ m\\\\\o\\d\\\\\\\\
001443351 007__ cr\un\nnnunnun
001443351 008__ 220103s2022\\\\sz\\\\\\ob\\\\000\0\eng\d
001443351 019__ $$a1290714101$$a1290815067$$a1290839178$$a1291316403$$a1294367606
001443351 020__ $$a9783030858551$$q(electronic bk.)
001443351 020__ $$a3030858553$$q(electronic bk.)
001443351 020__ $$z3030858545
001443351 020__ $$z9783030858544
001443351 0247_ $$a10.1007/978-3-030-85855-1$$2doi
001443351 035__ $$aSP(OCoLC)1290701977
001443351 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dDCT$$dOCLCF$$dN$T$$dAUD$$dOCLCO$$dOCLCQ
001443351 049__ $$aISEA
001443351 050_4 $$aHD38.5
001443351 08204 $$a658.50072/7$$223
001443351 1001_ $$aCohen, Maxime C.$$eauthor.
001443351 24510 $$aDemand prediction in retail :$$ba practical guide to leverage data and predictive analytics /$$cMaxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang.
001443351 260__ $$aCham, Switzerland :$$bSpringer,$$c2022.
001443351 300__ $$a1 online resource
001443351 336__ $$atext$$btxt$$2rdacontent
001443351 337__ $$acomputer$$bc$$2rdamedia
001443351 338__ $$aonline resource$$bcr$$2rdacarrier
001443351 347__ $$atext file$$bPDF$$2rda
001443351 4901_ $$aSpringer series in supply chain management,$$x2365-6409 ;$$vvolume 14
001443351 504__ $$aIncludes bibliographical references.
001443351 5050_ $$a1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics.
001443351 506__ $$aAccess limited to authorized users.
001443351 520__ $$aFrom data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.
001443351 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 10, 2022).
001443351 650_0 $$aBusiness logistics$$xStatistical methods.
001443351 650_0 $$aBusiness logistics$$xManagement.
001443351 650_0 $$aDemand (Economic theory)
001443351 650_6 $$aLogistique (Organisation)$$xMéthodes statistiques.
001443351 650_6 $$aLogistique (Organisation)$$xGestion.
001443351 650_6 $$aDemande (Théorie économique)
001443351 655_0 $$aElectronic books.
001443351 7001_ $$aGras, Paul-Emile,$$eauthor.
001443351 7001_ $$aPentecoste, Arthur,$$eauthor.
001443351 7001_ $$aZhang, Renyu,$$eauthor.
001443351 77608 $$iPrint version:$$z3030858545$$z9783030858544$$w(OCoLC)1262192722
001443351 830_0 $$aSpringer series in supply chain management ;$$vv. 14.$$x2365-6409
001443351 852__ $$bebk
001443351 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-85855-1$$zOnline Access$$91397441.1
001443351 909CO $$ooai:library.usi.edu:1443351$$pGLOBAL_SET
001443351 980__ $$aBIB
001443351 980__ $$aEBOOK
001443351 982__ $$aEbook
001443351 983__ $$aOnline
001443351 994__ $$a92$$bISE