AI RESEARCH
ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting
arXiv CS.AI
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ArXi:2603.20869v1 Announce Type: new 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.