# Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

arXiv: https://arxiv.org/abs/2301.11535 · HTML: https://mlsanity.com/paper/2301.11535/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 61 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 30 of 38)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://mlsanity.com/paper/2101.06861/) (2021)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)
- [Temporal Pattern Attention for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1809.04206/) (2018)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [Training individually fair ML models with Sensitive Subspace Robustness](https://mlsanity.com/paper/1907.00020/) (2019)
- [SOM-VAE: Interpretable Discrete Representation Learning on Time Series](https://mlsanity.com/paper/1806.02199/) (2018)
- [Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version](https://mlsanity.com/paper/2203.15737/) (2022)
- [Learning Fair Representations for Recommendation: A Graph-based Perspective](https://mlsanity.com/paper/2102.09140/) (2021)
- [AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version](https://mlsanity.com/paper/2112.11174/) (2021)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [Better Algorithms for Individually Fair k-Clustering](https://mlsanity.com/paper/2106.12150/) (2021)
- [Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning](https://mlsanity.com/paper/2008.12833/) (2020)
- [Fair Clustering Under a Bounded Cost](https://mlsanity.com/paper/2106.07239/) (2021)
- [Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version](https://mlsanity.com/paper/2204.03341/) (2022)
- [Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness](https://mlsanity.com/paper/2002.07738/) (2020)

## Built on by (most cited first, 8 of 8)

- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://mlsanity.com/paper/2311.06184/) (2023)
- [FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective](https://mlsanity.com/paper/2311.06190/) (2023)
- [SEMPO: Lightweight Foundation Models for Time Series Forecasting](https://mlsanity.com/paper/2510.19710/) (2025)
- [FairSTG: Countering performance heterogeneity via collaborative sample-level optimization](https://mlsanity.com/paper/2403.12391/) (2024)
- [Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift](https://mlsanity.com/paper/2407.13194/) (2024)
- [HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting](https://mlsanity.com/paper/2412.00316/) (2024)
- [Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks](https://mlsanity.com/paper/2412.16523/) (2024)
- [Gradient-based Model Shortcut Detection for Time Series Classification](https://mlsanity.com/paper/2510.10075/) (2025)

## Abstract (arXiv)

Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models since such models may attend/bias to certain (advantaged) variables. Addressing this unfairness problem is important for equally attending to all variables and avoiding vulnerable model biases/risks. However, fair MTS forecasting is challenging and has been less studied in the literature. To bridge such significant gap, we formulate the 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. FairFor is based on adversarial learning to generate both group-independent and group-relevant representations for the downstream forecasting. The framework first leverages a spectral relaxation of the K-means objective to infer variable correlations and thus to group variables. Then, it utilizes a filtering&fusion component to filter the group-relevant information and generate group-independent representations via orthogonality regularization. The group-independent and group-relevant representations form highly informative representations, facilitating to sharing knowledge from advantaged variables to disadvantaged variables to guarantee fairness. Extensive experiments on four public datasets demonstrate the effectiveness of our proposed FairFor for fair forecasting and significant performance improvement.