A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming

26 Jun 2020 Xiaoming Li Chun Wang Xiao Huang Yimin Nie

The conventional deep learning approaches for solving time-series problem such as long-short term memory (LSTM) and gated recurrent unit (GRU) both consider the time-series data sequence as the input with one single unit as the output (predicted time-series result). Those deep learning approaches have made tremendous success in many time-series related problems, however, this cannot be applied in data-driven stochastic programming problems since the output of either LSTM or GRU is a scalar rather than probability distribution which is required by stochastic programming model... (read more)

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