  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.
