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

TL;DR

ReLaMix is a novel neural network architecture designed to improve high-frequency financial time-series forecasting by effectively handling delayed and stale data observations through residual latency-aware mixing.

Contribution

It introduces ReLaMix, a lightweight extension of TimeMixer that incorporates residual refinement and bottleneck compression to enhance robustness against data delays in financial forecasting.

Findings

ReLaMix outperforms existing models on PAXGUSDT benchmark.

It maintains high accuracy across various delay ratios and horizons.

Demonstrates strong cross-asset generalization on BTCUSDT.

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 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.