深度学习与时间序列 Others 2017

2017

• Bendong Zhao; Huanzhang Lu; Shangfeng Chen; Junliang Liu; Dongya Wu (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics. 28(1), pages 162.169)

  Summary: This paper proposes a convolutional neural network (CNN) framework for time series classification. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains.

  Type: Convolutional neural network

• Ghulam Mohi Ud Din; Angelos K. Marnerides (2017). Short term power load forecasting using Deep Neural Networks. Computing, Networking and Communications (ICNC), 2017 International Conference on.

  Summary: This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead.

  Type: Recurrent neural network

• Qiang Wang ; Linqing Wang ; Jun Zhao ; Wei Wang (2017). Long-term time series prediction based on deep denoising recurrent temporal restricted Boltzmann machine network. Chinese Automation Congress (CAC), 2017

  Summary: The study proposes a deep denoising recurrent temporal restricted Boltzmann machine network for long-term prediction of time series.

  Notes:

  • Model train -> layer by layer
  • Model Fine-tuning connection weights -> Back-propagation

  Type: Recurrent restricted Boltzmann machine