Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021.

• Please RUN python3 ssmf.py, which starts processing DISEASE dataset and produces the SSMF results into "out" directory.
• Also, please RUN python3 ssmf.py --max_regimes 1 to compare its performance with SMF, which does not assume regime shifts.

SSMF expects 3-dimensional numpy.ndarray, whose last dimension corresponds to time points. If you have multi-column dataframes, utils.list2tensor helps convert your data to 3-dimensional array.

• --output_dir:
  Path of output directory (default: 'out')
• --periodicity:
  Length of periodicity (default: 52, for yearly patterns of weekly data)
• --n_components:
  Rank of matrix factorization (default: 10)
• --max_regimes:
  Maximum number of regimes (default: 50, set at 1 for SMF)
• --learning_rate:
  Size of gradient steps in each update (default: 0.2)
• --max_iter:
  Number of iterations to estimate new regimes (default: 1)
• --float_cost:
  Number of bits in MDL (default: 32)
• --update_freq:
  Interval of regime estimation (default: 1)

You will need to install following libraries,

• numpy: https://numpy.org
• pandas: https://github.com/pandas-dev/pandas
• tensorly: http://tensorly.org/stable/index.html
• sklearn: https://pykalman.github.io
• matplotlib: https://matplotlib.org
• tqdm: https://github.com/tqdm/tqdm

• Please download at:
  https://s3.amazonaws.com/tripdata/index.html
• Please download at:
  https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
• Disease: data/project_tycho.csv.gz. The original data can be found at:
  https://www.tycho.pitt.edu/data/

If you use this code for your research, please consider citing our paper.

@inproceedings{kawabata2021ssmf,
    title={SSMF: Shifting Seasonal Matrix Factorization},
    author={Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi},
    booktitle={NeurIPS},
    year={2021}