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Referierte Publikationen

Artikel in Zeitschriften (referiert) [2]

[Gum22] Gumz, J.; Fettermann, D.C.; Frazzon, E.M.; Kück, M.: Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions. In: Sustainability, 14(2022)20, pp. 34 (also project: PROGNOSE_NLD)
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[Scho14b] Scholz-Reiter, B.; Kück, M.; Lappe, D.: Prediction of customer demands for production planning - Automated selection and configuration of suitable prediction methods. In: CIRP Annals - Manufacturing Technology, 63(2014)1, pp. 417-420
[ BibTeX | DOI | www ]

Konferenzbeiträge (referiert) [4]

[Küc16a] Kück, M.; Crone, S. F.; Freitag, M.: Meta-Learning with Neural Networks and Landmarking for Forecasting Model Selection - An Empirical Evaluation of Different Feature Sets Applied to Industry Data. In: Estevez, P. A. (eds.): 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, N.N, 2016, pp. 1499-1506
[ BibTeX | DOI | www ]

[Küc14] Kück, M.; Scholz-Reiter, B.; Freitag, M.: Robust Methods for the Prediction of Customer Demands Based on Nonlinear Dynamical Systems. In: Windt, K. (eds.): Procedia CIRP. Proceedings of the 2nd CIRP Robust Manufacturing Conference (RoMac 2014), Elsevier, N.N, 2014, pp. 93-98
[ BibTeX | DOI | www ]

[Küc13] Kück, M.; Scholz-Reiter, B.: Forecasting of Customer Demands in Production Networks Based on Phase Space Reconstruction - An application to predict intermittent demand evolutions. In: Proceedings of the 33rd International Symposium on Forecasting. N.N, Seoul, South Korea, 2013, pp. 6
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[Küc13a] Kück, M.; Scholz-Reiter, B.: A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands. In: Wani, M. A.; Tecuci, G.; Boicu, M.; Kubat, M.; Khoshgoftaar, T. M.; Seliya, N. (eds.): Proceedings of the 12th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE Computer Society\'s Conference Publishing Services, N.N, 2013, pp. 160-165
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