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- W4386816086 abstract "A new approach to assessing states and predicting the behavior of weakly formalized dynamic processes based on predictive analytics is proposed. Technological processes of transportation at railway freight stations are considered as an object of forecasting. The proposed approach is based on the idea of the predictive models having structural sensitivity to concept drift in the technological process under study. Based on the structural sensitivity, the developed model entitled eTSS is able to detect predictor states. eTSS belongs to the class of adaptive evolving models based on hybridization of fuzzy Takagi-Sugeno systems with online machine learning methods and a special mechanism for incremental updating of the knowledge base. Online learning mechanism provides the ability of eTSS to adjust incrementally to the changing data flow. The experimental results collected from imitation modeling of the operation of Chelyabinsk-Glavniy station showed the advantage of the developed eTSS over similar evolving models in solving the tasks." @default.
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- W4386816086 date "2023-01-01" @default.
- W4386816086 modified "2023-09-27" @default.
- W4386816086 title "Temporal Prediction Models for Technological Processes Based on Predictive Analytics" @default.
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- W4386816086 doi "https://doi.org/10.1007/978-3-031-43792-2_18" @default.
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