Papers › Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast
Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic Forecast
Ji Qi, Tam Thuc Do, Mingxiao Liu, Zhuoshi Pan, Yuzhe Li, Gene Cheung, H. Vicky Zhao
To forecast traffic with both spatial and temporal dimensions, we unroll a mixed-graph-based optimization algorithm into a lightweight and interpretable transformer-like neural net. Specifically, we construct two graphs: an undirected graph 𝒢ᵘ capturing spatial correlations across geography, and a directed graph 𝒢ᵈ capturing sequential relationships over time. We formulate a prediction problem for the future samples of signal 𝐱, assuming it is "smooth" with respect to both 𝒢ᵘ and 𝒢ᵈ, where we design new ℓ₂ and ℓ₁-norm variational terms to quantify and promote signal smoothness (low-frequency reconstruction) on a directed graph. We construct an iterative algorithm based on alternating direction method of multipliers (ADMM), and unroll it into a feed-forward network for data-driven parameter learning. We insert graph learning modules for 𝒢ᵘ and 𝒢ᵈ, which are akin to the self-attention mechanism in classical transformers. Experiments show that our unrolled networks achieve competitive traffic forecast performance as state-of-the-art prediction schemes, while reducing parameter counts drastically. Our code is available in https://github.com/SingularityUndefined/Unrolling-GSP-STForecast.
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