Demand forecasting of individual probability density functions with machine learning
Felix Wick, Ulrich Kerzel, Martin Hahn, Moritz Wolf, Trapti Singhal, Daniel Stemmer, Jakob Ernst, Michael Feindt
| Year of publication: |
2021
|
|---|---|
| Authors: | Wick, Felix ; Kerzel, Ulrich ; Hahn, Martin ; Wolf, Moritz ; Singhal, Trapti ; Stemmer, Daniel ; Ernst, Jakob ; Feindt, Michael |
| Published in: |
Operations research forum. - Cham : Springer International Publishing, ISSN 2662-2556, ZDB-ID 2978290-9. - Vol. 2.2021, 3, Art.-No. 37, p. 1-39
|
| Subject: | Explainable machine learning | Retail demand forecasting | Probability distribution | Temporal confounding | Künstliche Intelligenz | Artificial intelligence | Prognoseverfahren | Forecasting model | Statistische Verteilung | Statistical distribution | Wahrscheinlichkeitsrechnung | Probability theory | Nachfrage | Demand | Einzelhandel | Retail trade | Theorie | Theory | Prognose | Forecast |
Online Resource
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1007/s43069-021-00079-8 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012589930