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Experimental Codes for Temporal Regularized Matrix Factoriztion for High-dimensional Time Series Prediction.

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This is the experimental code for the following paper:

* H.-F. Yu, N. Rao, and I. S. Dhillon. Temporal Regularized 
  Matrix Factoriztion for High-dimensional Time Series Prediction. Advances 
  in Neural Information Processing Systems (NIPS) 29, 2016.

Interfaces

The core codes of TRMF are implemented in C++. The original experiments are done using a Matlab interface. Upon the requests, I refactored the core C++ codes and created an easy-to-use Python interface (with Scipy/Numpy).

Citation

Please acknowledge the use of the code with a citation.
* H.-F. Yu, N. Rao, and I. S. Dhillon. Temporal Regularized 
  Matrix Factoriztion for High-dimensional Time Series Prediction. Advances 
  in Neural Information Processing Systems (NIPS) 29, 2016.
@inproceedings{hfy16a,
  title={Temporal Regularized Matrix Factoriztion for High-dimensional Time Series Prediction},
  author={Yu, Hsiang-Fu and Rao, Nikhil and Dhillon, Inderjit S.},
  booktitle = {Advances in Neural Information Processing Systems 28},
  year={2016}
}

If you have any questions regarding the code, feel free to contact Hsiang-Fu Yu (rofuyu at cs utexas edu).

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Experimental Codes for Temporal Regularized Matrix Factoriztion for High-dimensional Time Series Prediction.

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