ReCast: Reliability-aware Codebook Assisted Lightweight Time Series Forecasting

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, 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.