ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting
Xiang Ma, Taihua Chen, Pengcheng Wang, Xuemei Li, Caiming Zhang

TL;DR
ReCast is a lightweight, reliability-aware time series forecasting framework that encodes local patterns into a discrete codebook, enabling robust and efficient predictions in dynamic environments.
Contribution
The paper introduces ReCast, a novel framework that combines local pattern encoding with a reliability-aware codebook update for robust, resource-efficient time series forecasting.
Findings
ReCast outperforms state-of-the-art models in accuracy.
ReCast demonstrates superior efficiency and adaptability.
The reliability-aware codebook update enhances robustness to distribution shifts.
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,…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Traffic Prediction and Management Techniques · Forecasting Techniques and Applications
