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ERYGANOV, I. ROSECKÝ, M. ŠOMPLÁK, R. SMEJKALOVÁ, V.
Originální název
Forecasting the waste production hierarchical time series with correlation structure
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Continuous increase in society's prosperity causes overwhelming growth of the produced municipal solid waste. Circular economy initiatives help to solve this problem by creating closed production cycles, where the produced waste is recycled, or its energy is recovered. An embedment of such principles requires implementation of new waste management strategies. However, these novel strategies must be based on the accurate forecasts of future waste flows. Municipal solid waste production data demonstrate behavior of hierarchical time series. Among all possible approaches to hierarchical times series forecasting, this article is focused on the reconciliation of the base waste generation forecasts. The novel method, that is based on the game-theoretically optimal reconciliation of hierarchical time series, is presented. The modified approach enables to incorporate interdependencies between time series using correlation matrix and to obtain the forecasts corresponding to the unique solution of the optimization problem. The potential of the proposed abstract approach is demonstrated on the waste production data of paper, plastics (both primarily sorted by households), and mixed municipal solid waste from the Czech Republic.
Klíčová slova
Prediction; Waste management; Game theory; Zero-sum game; Nash equilibrium; Optimization
Autoři
ERYGANOV, I.; ROSECKÝ, M.; ŠOMPLÁK, R.; SMEJKALOVÁ, V.
Vydáno
2. 7. 2024
Nakladatel
SPRINGER
Místo
DORDRECHT
ISSN
1573-2924
Periodikum
OPTIMIZATION AND ENGINEERING
Číslo
July 2024
Stát
Spojené státy americké
Strany počet
23
URL
https://link.springer.com/article/10.1007/s11081-024-09898-0
BibTex
@article{BUT189262, author="Ivan {Eryganov} and Martin {Rosecký} and Radovan {Šomplák} and Veronika {Smejkalová}", title="Forecasting the waste production hierarchical time series with correlation structure", journal="OPTIMIZATION AND ENGINEERING", year="2024", number="July 2024", pages="23", doi="10.1007/s11081-024-09898-0", issn="1573-2924", url="https://link.springer.com/article/10.1007/s11081-024-09898-0" }