Streaming-Factor-Trajectory-Learning

This authors' official PyTorch implementation for paper: Streaming Factor Trajectory Learning for Temporal Tensor Decomposition(NeurIPS 2023)

Streaming Learning of Temporal Tensor Data

Each factor is a time-varing and uncertainty-aware function

To model the temporal tensor data, we assign each tensor factor with Temporal-Gaussian Process priors and model it as the continuous-time-varing trajectory.

Online inference for streaming data:

The inference of the factor trajectory is in a streaming and online manner.

Example of learned functional frajectories of factors from real-world data.

Requirements:

The project is mainly built with pytorch 1.10.1 under python 3. Besides that, make sure to install tqdm and tensorly before running the project.

Instructions:

1. Clone this repository.
2. To play with the code quickly with visulization of factors, we offer several notebooks at notebook(on synthetic & real data)
3. To run the real-world datasets with scripts, see my_script.sh for example.
4. To tune the (hyper)parametrs of model, modify the .yaml files in config folder
5. To apply the model on your own dataset, please follow the ./data/process_script/beijing_15k_process.ipynb or ./data/synthetic/simu_data_generate_CP_r1.ipynb to process the raw data into appropriate format.

Please check our paper for more details.

Citation

Please cite our work if you would like it

@misc{fang2023streaming,
      title={Streaming Factor Trajectory Learning for Temporal Tensor Decomposition}, 
      author={Shikai Fang and Xin Yu and Shibo Li and Zheng Wang and Robert Kirby and Shandian Zhe},
      year={2023},
      eprint={2310.17021},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}