揭示掩码自编码器在交通预测中的能力
Revealing the Power of Masked Autoencoders in Traffic Forecasting
AI总结：
针对交通预测中的数据稀缺和模型稳定性问题，提出即插即用的时空掩码自编码器框架STMAE，通过双重掩码预训练与微调集成，显著提升多种时空模型的预测精度。
摘要：
交通预测对城市规划至关重要，需要对城市区域间的时空交通模式进行准确预测。现有研究主要集中于设计复杂模型，以显式捕捉变量间的时空依赖关系。然而，该领域面临数据稀缺和模型稳定性的挑战，导致性能提升受限。为解决这些问题，我们提出了时空掩码自编码器（STMAE），这是一个即插即用的框架，旨在增强现有时空模型在交通预测上的表现。STMAE包含两个学习阶段。在预训练阶段，编码器处理由双重掩码策略生成的部分可见交通数据，该策略包括基于有偏随机游走的空问掩码和基于分块的时间掩码。随后，两个解码器分别从空间和时间角度重建被掩码的部分。微调阶段保留预训练的编码器，并将其与现有骨干网络的解码器集成，以提高预测精度。我们在交通基准上的结果表明，STMAE能大幅增强各种时空模型的预测能力。
英文摘要：
Traffic forecasting, crucial for urban planning, requires accurate predictions of spatial-temporal traffic patterns across urban areas. Existing research mainly focuses on designing complex models that capture spatial-temporal dependencies among variables explicitly. However, this field faces challenges related to data scarcity and model stability, which results in limited performance improvement. To address these issues, we propose Spatial-Temporal Masked AutoEncoders (STMAE), a plug-and-play framework designed to enhance existing spatial-temporal models on traffic prediction. STMAE consists of two learning stages. In the pretraining stage, an encoder processes partially visible traffic data produced by a dual-masking strategy, including biased random walk-based spatial masking and patch-based temporal masking. Subsequently, two decoders aim to reconstruct the masked counterparts from both spatial and temporal perspectives. The fine-tuning stage retains the pretrained encoder and integrates it with decoders from existing backbones to improve forecasting accuracy. Our results on traffic benchmarks show that STMAE can largely enhance the forecasting capabilities of various spatial-temporal models.
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