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 1to 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}
