Matches in SemOpenAlex for { <https://semopenalex.org/work/W3016366056> ?p ?o ?g. }
- W3016366056 endingPage "1438" @default.
- W3016366056 startingPage "1420" @default.
- W3016366056 abstract "Abstract Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry." @default.
- W3016366056 created "2020-04-24" @default.
- W3016366056 creator A5050425933 @default.
- W3016366056 creator A5087268323 @default.
- W3016366056 date "2020-10-01" @default.
- W3016366056 modified "2023-10-13" @default.
- W3016366056 title "Daily retail demand forecasting using machine learning with emphasis on calendric special days" @default.
- W3016366056 cites W1471542436 @default.
- W3016366056 cites W1586335931 @default.
- W3016366056 cites W1895650610 @default.
- W3016366056 cites W1981144978 @default.
- W3016366056 cites W1981553219 @default.
- W3016366056 cites W1988277750 @default.
- W3016366056 cites W1991502340 @default.
- W3016366056 cites W1992880712 @default.
- W3016366056 cites W1996190164 @default.
- W3016366056 cites W2003859567 @default.
- W3016366056 cites W2006913962 @default.
- W3016366056 cites W2014928429 @default.
- W3016366056 cites W2015393497 @default.
- W3016366056 cites W2016210396 @default.
- W3016366056 cites W2018559635 @default.
- W3016366056 cites W2040395995 @default.
- W3016366056 cites W2054475196 @default.
- W3016366056 cites W2055311466 @default.
- W3016366056 cites W2056043406 @default.
- W3016366056 cites W2058498909 @default.
- W3016366056 cites W2063251682 @default.
- W3016366056 cites W2063871438 @default.
- W3016366056 cites W2064675550 @default.
- W3016366056 cites W2075965721 @default.
- W3016366056 cites W2090184979 @default.
- W3016366056 cites W2094515728 @default.
- W3016366056 cites W2097376429 @default.
- W3016366056 cites W2104908927 @default.
- W3016366056 cites W2114062456 @default.
- W3016366056 cites W2124030982 @default.
- W3016366056 cites W2132809790 @default.
- W3016366056 cites W2133566740 @default.
- W3016366056 cites W2138731244 @default.
- W3016366056 cites W2140345205 @default.
- W3016366056 cites W2149905014 @default.
- W3016366056 cites W2153787847 @default.
- W3016366056 cites W2159613688 @default.
- W3016366056 cites W2162174678 @default.
- W3016366056 cites W2166270823 @default.
- W3016366056 cites W2170391700 @default.
- W3016366056 cites W2190044943 @default.
- W3016366056 cites W2196335450 @default.
- W3016366056 cites W2258706126 @default.
- W3016366056 cites W2329494830 @default.
- W3016366056 cites W2343091109 @default.
- W3016366056 cites W2409919419 @default.
- W3016366056 cites W2471218770 @default.
- W3016366056 cites W2562695409 @default.
- W3016366056 cites W2581191963 @default.
- W3016366056 cites W2581984534 @default.
- W3016366056 cites W2770188460 @default.
- W3016366056 cites W2794778778 @default.
- W3016366056 cites W2795233095 @default.
- W3016366056 cites W2808800115 @default.
- W3016366056 cites W2907251284 @default.
- W3016366056 cites W2959101608 @default.
- W3016366056 cites W3123170997 @default.
- W3016366056 cites W4213041519 @default.
- W3016366056 doi "https://doi.org/10.1016/j.ijforecast.2020.02.005" @default.
- W3016366056 hasPublicationYear "2020" @default.
- W3016366056 type Work @default.
- W3016366056 sameAs 3016366056 @default.
- W3016366056 citedByCount "63" @default.
- W3016366056 countsByYear W30163660562020 @default.
- W3016366056 countsByYear W30163660562021 @default.
- W3016366056 countsByYear W30163660562022 @default.
- W3016366056 countsByYear W30163660562023 @default.
- W3016366056 crossrefType "journal-article" @default.
- W3016366056 hasAuthorship W3016366056A5050425933 @default.
- W3016366056 hasAuthorship W3016366056A5087268323 @default.
- W3016366056 hasConcept C144133560 @default.
- W3016366056 hasConcept C162324750 @default.
- W3016366056 hasConcept C162853370 @default.
- W3016366056 hasConcept C177454536 @default.
- W3016366056 hasConcept C193809577 @default.
- W3016366056 hasConcept C2983523559 @default.
- W3016366056 hasConcept C41008148 @default.
- W3016366056 hasConcept C54750564 @default.
- W3016366056 hasConcept C76155785 @default.
- W3016366056 hasConceptScore W3016366056C144133560 @default.
- W3016366056 hasConceptScore W3016366056C162324750 @default.
- W3016366056 hasConceptScore W3016366056C162853370 @default.
- W3016366056 hasConceptScore W3016366056C177454536 @default.
- W3016366056 hasConceptScore W3016366056C193809577 @default.
- W3016366056 hasConceptScore W3016366056C2983523559 @default.
- W3016366056 hasConceptScore W3016366056C41008148 @default.
- W3016366056 hasConceptScore W3016366056C54750564 @default.
- W3016366056 hasConceptScore W3016366056C76155785 @default.
- W3016366056 hasIssue "4" @default.
- W3016366056 hasLocation W30163660561 @default.
- W3016366056 hasOpenAccess W3016366056 @default.