ReLaMix：残差时延感知混合用于延迟鲁棒的金融时间序列预测
ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting
AI总结：
本文提出ReLaMix网络，通过残差细化和可学习瓶颈压缩提升延迟观测下的信号恢复能力，在大规模PAXGUSDT数据集上验证了其在多种延迟比例和预测时间跨度下的卓越性能。
摘要：
现实高频市场中的金融时间序列预测常受异步数据采集和传输延迟导致的延迟或部分过时观测阻碍。为更真实地反映这种情况，本文研究了一种模拟延迟设置，其中部分历史信号被零阶保持机制（ZOH）破坏，通过逐步停滞伪影显著增加预测难度。本文提出ReLaMix（残差时延感知混合网络），作为TimeMixer的轻量级扩展，整合了可学习瓶颈压缩与残差细化，以在延迟观测下实现稳健的信号恢复。ReLaMix通过残差混合增强，明确抑制重复过时值的冗余，同时保留信息市场动态。在大规模第二分辨率PAXGUSDT基准上的实验表明，ReLaMix在多种延迟比例和预测时间跨度下均取得最佳准确性，优于强混合器和Transformer基线模型，且参数量显著更少。此外，对BTCUSDT的额外评估证实了所提框架的跨资产泛化能力。这些结果突显了残差瓶颈混合在现实时延诱导过时情况下高频率金融预测中的有效性。
英文摘要：
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 ratios and prediction horizons, outperforming strong mixer and Transformer baselines with substantially fewer parameters. Moreover, additional evaluations on BTCUSDT confirm the cross-asset generalization ability of the proposed framework. These results highlight the effectiveness of residual bottleneck mixing for high-frequency financial forecasting under realistic latency-induced staleness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
