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- W3133038042 abstract "The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation." @default.
- W3133038042 created "2021-03-01" @default.
- W3133038042 creator A5050386733 @default.
- W3133038042 date "2021-06-30" @default.
- W3133038042 modified "2023-09-25" @default.
- W3133038042 title "Artificial intelligence-based hybrid forecasting models for manufacturing systems" @default.
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- W3133038042 doi "https://doi.org/10.17531/ein.2021.2.6" @default.
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