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dataset Add files via upload Aug 4, 2017
test Add files via upload Aug 4, 2017
train Add files via upload Aug 4, 2017 Update Aug 4, 2017

State Frequency Memory recurrent network for stock price prediction

Author: Liheng Zhang, Date: 08/03/2017

This is the project for the following paper:

Liheng Zhang, Charu Aggarwal, Guo-Jun Qi, Stock Price Prediction via Discovering Multi-Frequency Trading Patterns,
in Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, Nova Scotia,
Canada, August 13-17, 2017.

Questions about the source codes can be directed to Liheng Zhang at

For more applications with SFM, please refer to:

Hao Hu, Guo-Jun Qi. State-Frequency Memory Recurrent Neural Networks, in Proceedings of International Conference
on Machine Learning (ICML 2017), Sydney, Australia, August 6-11, 2017.


  • Python == 2.7
  • Keras == 1.0.1
  • Theano == 0.9

Prepare the data

cd dataset; python

Test with pretrained model

cd test
python --step=1

The model for n-step prediction is specified with --step. Models for 1-step, 3-step and 5-step prediction are provided.

To visualize the predicted results:

python test --step=1 --visualization=true


cd train
python --step=3 --hidden_dim=50 --freq_dim=10 --niter=4000 --learning_rate=0.01


The codes are expired for Keras >= 2.0.0. Codes for the latest version of Keras will be released.