A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic
Programming
- URL: http://arxiv.org/abs/2006.16845v1
- Date: Fri, 26 Jun 2020 15:42:59 GMT
- Title: A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic
Programming
- Authors: Xiaoming Li, Chun Wang, Xiao Huang, Yimin Nie
- Abstract summary: We propose an innovative data-driven dynamic programming (DD-DSP) framework for time-series decision-making problem.
Specifically, we devise a deep neural network that integrates GRU and Gaussian Mixture Model (GMM)
Our framework is superior to data-driven optimization based on LSTM with the vehicle average moving lower than LSTM.
- Score: 15.517550827358104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. To fill the gap, in this work, we
propose an innovative data-driven dynamic stochastic programming (DD-DSP)
framework for time-series decision-making problem, which involves three
components: GRU, Gaussian Mixture Model (GMM) and SP. Specifically, we devise
the deep neural network that integrates GRU and GMM which is called GRU-based
Mixture Density Network (MDN), where GRU is used to predict the time-series
outcomes based on the recent historical data, and GMM is used to extract the
corresponding probability distribution of predicted outcomes, then the results
will be input as the parameters for SP. To validate our approach, we apply the
framework on the car-sharing relocation problem. The experiment validations
show that our framework is superior to data-driven optimization based on LSTM
with the vehicle average moving lower than LSTM.
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