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Corpus ID: 220265553

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

@article{Li2020AGM,
title={A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming},
author={Xiaoming Li and Chenshu Wang and X. Huang and Y. Nie},
journal={ArXiv},
year={2020},
volume={abs/2006.16845}
}

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… CONTINUE READING