[2603.20869] ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting

Computer Science > Artificial Intelligence arXiv:2603.20869 (cs) [Submitted on 21 Mar 2026] Title:ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting Authors:Tianyou Lai, Wentao Yue, Jiayi Zhou, Chaoyuan Hao, Lingke Chang, Qingyu Mao, Zhibo Niu, Qilei Li View a PDF of the paper titled ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting, by Tianyou Lai and 7 other authors View PDF HTML (experimental) Abstract:Financial time-series forecasting in real-world high-frequency markets is often hindered by delayed or partially stale observations caused by asynchronous data acquisition and transmission latency. To better reflect such practical conditions, we investigate a simulated delay setting where a portion of historical signals is corrupted by a Zero-Order Hold (ZOH) mechanism, significantly increasing forecasting difficulty through stepwise stagnation artifacts. In this paper, we propose ReLaMix (Residual Latency-Aware Mixing Network), a lightweight extension of TimeMixer that integrates learnable bottleneck compression with residual refinement for robust signal recovery under delayed observations. ReLaMix explicitly suppresses redundancy from repeated stale values while preserving informative market dynamics via residual mixing enhancement. Experiments on a large-scale second-resolution PAXGUSDT benchmark demonstrate that ReLaMix consistently achieves state-of-the-art accuracy across multiple delay r...