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}
}
  • Xiaoming Li, Chenshu Wang, +1 author Y. Nie
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • ArXiv
  • 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

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