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- W2912011987 abstract "We propose a simulation-based prediction framework which can quantify the prediction uncertainty of system future response and further guide operational decisions for complex stochastic systems. Specifically, by exploring the underlying generative process of real-world data streams, we first develop a nonparametric input model which can capture the important properties, including non-stationarity, skewness, component-wise and time dependence. It can improve the prediction accuracy, and the posterior predictive distribution can quantify the prediction uncertainty accounting for both input and stochastic uncertainties. Then, we propose the simulation-based prediction framework which can efficiently search for the optimal operational decisions hedging against the prediction uncertainty and minimizing the expected cost occurring in the planning horizon. The empirical study demonstrates that our approach has promising performance." @default.
- W2912011987 created "2019-02-21" @default.
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- W2912011987 date "2018-12-09" @default.
- W2912011987 modified "2023-10-14" @default.
- W2912011987 title "A simulation-based prediction framework for stochastic system dynamic risk management" @default.
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- W2912011987 doi "https://doi.org/10.5555/3320516.3320744" @default.
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