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

2026-03-21

AI Summary

This work addresses the challenge of observation lag in high-frequency financial markets caused by asynchronous sampling and transmission delays. To mitigate this issue, the authors propose ReLaMix, a novel architecture built upon TimeMixer that incorporates a learnable bottleneck compression module and a residual mixture enhancement mechanism. ReLaMix employs a lightweight, delay-aware temporal mixing design to explicitly suppress redundant information from stale observations while preserving critical market dynamics. Evaluated on the PAXG/USDT second-level dataset, ReLaMix achieves state-of-the-art performance across various delay ratios and prediction horizons with fewer parameters. Furthermore, its cross-asset generalization capability is validated on the BTC/USDT pair, demonstrating robustness beyond the training asset.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• financial time-series forecasting
• transmission latency
• delayed observations
• Zero-Order Hold
• high-frequency markets

Innovation

• Residual Latency-Aware Mixing
• Zero-Order Hold
• Bottleneck Compression
• Delay-Robust Forecasting
• High-Frequency Financial Time-Series