Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python.

• Python 2.7
• Numpy
• Matplotlib
• pandas
• Expsuite (included in this repository)

• NYC taxi passenger count
• Prediction of the New York City taxi passenger data. left. Example portion of taxi passenger data (aggregated at 30 min intervals).
• public data stream provided by the New York City Transportation Authority
• preprocessed (aggregated at 30 min intervals) by Cui, Yuwei, et al. in "A comparative study of HTM and other neural network models for online sequence learning with streaming data." Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016. , code

• Online Sequential Extreme Learning Machine (OS-ELM)
  • Liang, Nan-Ying, et al. "A fast and accurate online sequential learning algorithm for feedforward networks." IEEE Transactions on neural networks 17.6 (2006): 1411-1423.
• Fully Online Sequential Extreme Learning Machine (FOS-ELM)
  • Wong, Pak Kin, et al. "Adaptive control using fully online sequential-extreme learning machine and a case study on engine air-fuel ratio regulation." Mathematical Problems in Engineering 2014 (2014).
• Normalized FOS-ELM (NFOS-ELM) (proposed)
  • FOS-ELM + Layer Normalization + forgetting factor
• Normalized Auto-encoded FOS-ELM (NAOS-ELM) (proposed)
  • FOS-ELM + Layer Normalization + forgetting factor + weight auto-encoding (input->hidden)
• Online Recurrent Extreme Learning Machine (OR-ELM) (proposed)
  • FOS-ELM + Layer Normalization + forgetting factor + weight auto-encoding (input->hidden, hidden->hidden)
  • This is for training recurrent neural networks (RNNs)

Run prediction code:

python run.py -a ORELM

Plot performance comparison:

python plotResults.py

• Prediction from OR-ELM

• Performance comparison
  • FOS-ELM and proposed variants including OR-ELM

• Rewrite this code with Pytorch for GPU acceleration

If you use this code, please cite our paper "Online Recurrent Extreme Learning Machine and its Application to time-series Prediction" in IEEE Access.

Paper URL: http://ieeexplore.ieee.org/abstract/document/7966094/ http://rit.kaist.ac.kr/home/International_Conference?action=AttachFile&do=get&target=paper_0411.pdf

Park, Jin-Man, and Jong-Hwan Kim. "Online recurrent extreme learning machine and its application to time-series prediction." Neural Networks (IJCNN), 2017 International Joint Conference on. IEEE, 2017.

This work was supported by the ICT R&D program of MSIP/IITP. [2016-0-00563, Research on Adaptive Machine Learning Technology Development for Intelligent Autonomous Digital Companion]