ReCast: 基于可靠性感知的代码本辅助轻量时间序列预测
ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting
AI总结：
ReCast通过可靠性感知的代码本辅助方法，实现轻量且稳健的时间序列预测，提升预测准确性和适应性。
摘要：
时间序列预测在各种领域应用中至关重要。传统方法通常依赖于将时间序列分解为趋势、季节性和残差成分，这在由局部、复杂且高度动态模式主导的现实世界序列中变得无效。此外，此类方法的高模型复杂性限制了它们在实时或资源受限环境中的应用。在本工作中，我们提出了一种新的可靠性感知代码本辅助时间序列预测框架（ReCast），通过利用重复的局部形状实现轻量且稳健的预测。ReCast通过使用可学习的代码本进行分块量化，将局部模式编码为离散嵌入，从而紧凑地捕捉到稳定的常规结构。为了补偿未被量化保留的残差变化，ReCast采用包含量化路径和残差路径的双路径架构。量化路径用于高效建模常规结构，残差路径用于重建不规则波动。ReCast的核心贡献是可靠性感知的代码本更新策略，该策略通过加权修正逐步优化代码本。这些修正权重通过融合多个可靠性因素，由互补视角的分布鲁棒优化（DRO）方案得出，确保适应非平稳性和对分布偏移的鲁棒性。大量实验表明，ReCast在准确率、效率和对分布偏移的适应性方面均优于最先进（SOTA）模型。
英文摘要：
Time series forecasting is crucial for applications in various domains. Conventional methods often rely on global decomposition into trend, seasonal, and residual components, which become ineffective for real-world series dominated by local, complex, and highly dynamic patterns. Moreover, the high model complexity of such approaches limits their applicability in real-time or resource-constrained environments. In this work, we propose a novel \textbf{RE}liability-aware \textbf{C}odebook-\textbf{AS}sisted \textbf{T}ime series forecasting framework (\textbf{ReCast}) that enables lightweight and robust prediction by exploiting recurring local shapes. ReCast encodes local patterns into discrete embeddings through patch-wise quantization using a learnable codebook, thereby compactly capturing stable regular structures. To compensate for residual variations not preserved by quantization, ReCast employs a dual-path architecture comprising a quantization path for efficient modeling of regular structures and a residual path for reconstructing irregular fluctuations. A central contribution of ReCast is a reliability-aware codebook update strategy, which incrementally refines the codebook via weighted corrections. These correction weights are derived by fusing multiple reliability factors from complementary perspectives by a distributionally robust optimization (DRO) scheme, ensuring adaptability to non-stationarity and robustness to distribution shifts. Extensive experiments demonstrate that ReCast outperforms state-of-the-art (SOTA) models in accuracy, efficiency, and adaptability to distribution shifts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
