Source code for the paper, "Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective", accepted by TKDE.
In this work, we formulate the MTS fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables. Accordingly, we propose a novel framework, named FairFor, for fairness-aware MTS forecasting, i.e., fair MTS forecasting.
- Python 3.6
- matplotlib == 3.3.4
- numpy == 1.19.5
- pandas == 1.1.5
- scikit_learn == 0.24.2
- torch == 1.8.0
- PeMSD7(M) - https://dot.ca.gov/programs/traffic-operations/mpr/pems-source
- Solar-Energy - http://www.nrel.gov/grid/solar-power-data.html
- Traffic - https://archive.ics.uci.edu/ml/datasets/PEMS-SF
- ECG5000 - http://www.timeseriesclassification.com/description.php?Dataset=ECG5000
- LSTNet - https://github.com/laiguokun/LSTNet
- TPA-LSTM - https://github.com/shunyaoshih/TPA-LSTM
- TS2VEC - https://github.com/yuezhihan/ts2vec
- Informer - https://github.com/zhouhaoyi/Informer2020
- Pyraformer - https://github.com/ant-research/Pyraformer
- MTGNN - https://github.com/nnzhan/MTGNN
- StemGNN - https://github.com/microsoft/StemGNN
- AGCRN - https://github.com/LeiBAI/AGCRN
If you find our work useful, please consider citing the following paper
@article{DBLP:journals/corr/abs-2301-11535,
author = {Hui He and
Qi Zhang and
Shoujin Wang and
Kun Yi and
Zhendong Niu and
Longbing Cao},
title = {Learning Informative Representation for Fairness-aware Multivariate
Time-series Forecasting: {A} Group-based Perspective},
journal = {CoRR},
volume = {abs/2301.11535},
year = {2023}
}