ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting

• URL: http://arxiv.org/abs/2511.11991v1
• Date: Sat, 15 Nov 2025 02:11:05 GMT
• Title: ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting
• Authors: Xiang Ma, Taihua Chen, Pengcheng Wang, Xuemei Li, Caiming Zhang,
• Abstract summary: We propose a novel textbfREliability-aware textbfCodebook-textbfASsisted textbfTime series forecasting framework (textbfReCast)<n>ReCast encodes local patterns into discrete embeddings through patch-wise quantization using a learnable codebook.<n>Experiments demonstrate that ReCast outperforms state-of-the-art (SOTA) models in accuracy, efficiency, and adaptability to distribution shifts.
• Score: 23.30244577147735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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