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.

翻译：时间序列预测在众多领域应用中至关重要。传统方法通常依赖于将序列全局分解为趋势、季节性和残差分量，这对于由局部、复杂且高度动态模式主导的真实世界序列往往失效。此外，这类方法的高模型复杂度限制了其在实时或资源受限环境中的适用性。本文提出了一种新颖的**可**靠性感知**码**本**辅**助时间序列预测框架（**ReCast**），该框架通过利用重复出现的局部形态，实现了轻量且鲁棒的预测。ReCast通过使用可学习码本进行分块量化，将局部模式编码为离散嵌入，从而紧凑地捕获稳定的规律结构。为了补偿量化未能保留的残差变化，ReCast采用了一种双路径架构，包含一条用于高效建模规律结构的量化路径和一条用于重建不规则波动的残差路径。ReCast的一个核心贡献是一种可靠性感知的码本更新策略，该策略通过加权修正逐步优化码本。这些修正权重通过分布鲁棒优化（DRO）方案融合来自互补视角的多个可靠性因子而得到，从而确保了对非平稳性的适应能力和对分布偏移的鲁棒性。大量实验表明，ReCast在准确性、效率以及对分布偏移的适应性方面均优于当前最先进的（SOTA）模型。