Demand forecasting of individual probability density functions with machine learning
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 | Prognoseverfahren | Forecasting model | Statistische Verteilung | Statistical distribution | Nachfrage | Demand | Künstliche Intelligenz | Artificial intelligence | Theorie | Theory |
-
Combination forecasts of tourism demand with machine learning models
Claveria, Oscar, (2016)
-
A score-driven model of short-term demand forecasting for retail distribution centers
Hoeltgebaum, Henrique, (2021)
-
Modeling commodity value at risk with Psi Sigma neural networks using open-high-low-close data
Sermpinis, Georgios, (2015)
- More ...
-
Prognosen bewerten : Statistische Grundlagen und praktische Tipps
Feindt, Michael, (2015)
-
Prognosen bewerten : statistische Grundlagen und praktische Tipps
Feindt, Michael, (2015)
-
Doetsch, Matthaeus, (1937)
- More ...