A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction
<|reference_start|>A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction: Accurate traffic prediction faces significant challenges, necessitating a deep understanding of both temporal and spatial cues and their complex interactions across multiple variables. Recent advancements in traffic prediction systems are primarily due to the development of complex sequence-centric models. However, existing approaches often embed multiple variables and spatial relationships at each time step, which may hinder effective variable-centric learning, ultimately leading to performance degradation in traditional traffic prediction tasks. To overcome these limitations, we introduce variable-centric and prior knowledge-centric modeling techniques. Specifically, we propose a Heterogeneous Mixture of Experts (TITAN) model for traffic flow prediction. TITAN initially consists of three experts focused on sequence-centric modeling. Then, designed a low-rank adaptive method, TITAN simultaneously enables variable-centric modeling. Furthermore, we supervise the gating process using a prior knowledge-centric modeling strategy to ensure accurate routing. Experiments on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate that TITAN effectively captures variable-centric dependencies while ensuring accurate routing. Consequently, it achieves improvements in all evaluation metrics, ranging from approximately 4.37\% to 11.53\%, compared to previous state-of-the-art (SOTA) models. The code is open at \href{https://github.com/sqlcow/TITAN}{https://github.com/sqlcow/TITAN}.<|reference_end|>
@article{wang2024a,
    title={A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts
  for Traffic Flow Prediction},
    author={Guangyu Wang, Yujie Chen, Ming Gao, Zhiqiao Wu, Jiafu Tang, Jiabi Zhao},
    journal={arXiv preprint arXiv:2409.17440},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17440},
    primaryClass={cs.AI}
}