TAEGCN combines masked multi-head attention and a GRU-driven evolving graph to model temporal and spatial dependencies in multivariate traffic forecasting, reporting lower errors than baselines on METR-LA and PEMS-BAY.
Machine Learning 108, 1421–1441 (2019)
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Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting
TAEGCN combines masked multi-head attention and a GRU-driven evolving graph to model temporal and spatial dependencies in multivariate traffic forecasting, reporting lower errors than baselines on METR-LA and PEMS-BAY.