ButterMamba: Butterworth-Enhanced Spatial-Temporal Mamba for Efficient Traffic Flow Prediction

arXiv:2608.29658 · physics.soc-ph, cs.LG, physics.data-an · Submitted 2026-08-30 · Read on arXiv

physics.soc-ph, cs.LG, physics.data-an

Submitted: 2026-08-30

Updated: 2026-08-30

Comments: 9 pages, 6 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• State Space Model — 11× in this paper
• Transformer — 11× in this paper · explained in 2 episodes
• Intelligent Transportation Systems — 7× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Butterworth Spectral Filtering — 4× in this paper
• Decoupling — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Spatial-Temporal State Mixer — 4× in this paper
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Attention Mechanism — 3× in this paper · explained in 3 episodes
• GNNs — 3× in this paper · explained in 2 episodes
• State Space Models (SSMs) — 3× in this paper · explained in 4 episodes
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Convolutional Neural Networks (CNNs) — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Convolutional Recurrent Neural Network — 2× in this paper
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Mean Absolute Error — 1× in this paper
• Mean Absolute Percentage Error — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• HiPPO: Recurrent Memory with Optimal Polynomial Projections
• Efficiently Modeling Long Sequences with Structured State Spaces
• STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
• Decoupled Weight Decay Regularization
• ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling
• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

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