Communications for Statistical Applications and Methods

  • 제28권1호
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  • Pages.39-57
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  • 2021
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Two-dimensional attention-based multi-input LSTM for time series prediction

  • Kim, Eun Been (Department of Applied Statistics, Chung-Ang University) ;
  • Park, Jung Hoon (Department of Applied Statistics, Chung-Ang University) ;
  • Lee, Yung-Seop (Department of Statistics, Dongguk University) ;
  • Lim, Changwon (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2020.08.19
  • 심사 : 2020.12.23
  • 발행 : 2021.01.31
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초록

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

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참고문헌

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