Spatio-temporal forecasting, pivotal in numerous fields, hinges on the delicate equilibrium between isolating nuanced patterns and sifting out noise. To tackle this, we introduce Sparse Regression-based Vector Quantization (SVQ), a novel technique that leverages sparse regression for succinct representation, an approach theoretically and practically favored over classical clustering-based vector quantization methods. This approach preserves critical details from the original vectors using a regression model while filtering out noise via sparse design. Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook. This approach not only substantially cuts down on computational costs but also grants SVQ differentiability and training simplicity, resulting in a notable enhancement of performance. Our empirical studies on five spatial-temporal benchmark datasets demonstrate that SVQ achieves state-of-the-art results. Specifically, on the WeatherBench-S temperature dataset, SVQ improves the top baseline by 7.9%. In video prediction benchmarks-Human, KTH, and KittiCaltech-it reduces MAE by an average of 9.4% and improves image quality by 17.3% (LPIPS).

翻译：时空预测是众多领域中的关键任务，其核心在于精确平衡细微模式识别与噪声过滤之间的关系。为解决这一问题，我们提出基于稀疏回归的向量量化方法（SVQ），这项新技术利用稀疏回归实现简洁表征，在理论和实践上均优于传统基于聚类的向量量化方法。该方法通过回归模型保留原始向量的关键细节，同时借助稀疏设计过滤噪声。此外，我们采用双层MLP与大规模码本相结合的混合方式近似稀疏回归过程。这种方法不仅显著降低计算成本，还赋予SVQ可微分性与训练简便性，从而带来性能的显著提升。在五个时空基准数据集上的实证研究表明，SVQ达到了当前最优性能。具体而言，在WeatherBench-S温度数据集上，SVQ相比最佳基线方法提升7.9%；在视频预测基准Human、KTH和KittiCaltech上，该方法使MAE平均降低9.4%，图像质量（LPIPS）提升17.3%。