ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting
arXiv:2603.20869v1 Announce Type: new 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
arXiv:2603.20869v1 Announce Type: new 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.
Executive Summary
This article proposes ReLaMix, a novel deep learning model designed for financial time-series forecasting in high-frequency markets with delayed or stale observations. ReLaMix combines TimeMixer with learnable bottleneck compression and residual refinement to recover signals under delayed observations. The model outperforms state-of-the-art baselines, including mixers and Transformers, on a large-scale PAXGUSDT benchmark and exhibits cross-asset generalization ability on BTCUSDT. The results highlight ReLaMix's effectiveness under realistic latency-induced staleness. The model's efficiency, with fewer parameters, is a significant advantage in real-world applications. While the article presents promising results, further investigation into the model's robustness and adaptability to varying market conditions is warranted.
Key Points
- ▸ ReLaMix integrates learnable bottleneck compression with residual refinement for robust signal recovery under delayed observations.
- ▸ The model outperforms state-of-the-art baselines on a large-scale PAXGUSDT benchmark and demonstrates cross-asset generalization ability on BTCUSDT.
- ▸ ReLaMix achieves high accuracy with substantially fewer parameters than existing models.
Merits
Strength
ReLaMix's effectiveness in recovering signals under delayed observations, as demonstrated by its superior performance on the PAXGUSDT benchmark.
Efficiency
ReLaMix's ability to achieve high accuracy with significantly fewer parameters than existing models, making it more suitable for real-world applications.
Demerits
Limitation
The model's robustness and adaptability to varying market conditions, such as changes in trading patterns or unexpected events, remain unexplored in this article.
Generalizability
While ReLaMix demonstrates cross-asset generalization ability, further investigation into its applicability to diverse financial markets and instruments is necessary.
Expert Commentary
The proposed ReLaMix model represents a significant advancement in financial time-series forecasting under delayed observations. The article's results demonstrate the model's effectiveness and efficiency, making it a promising solution for real-world applications. However, further research is necessary to explore the model's robustness and adaptability to varying market conditions. The implications of this study are substantial, with potential applications in both practical and policy contexts.
Recommendations
- ✓ Future studies should investigate ReLaMix's robustness and adaptability to varying market conditions, such as changes in trading patterns or unexpected events.
- ✓ The proposed ReLaMix model should be compared with other state-of-the-art models in more diverse financial markets and instruments to assess its cross-asset generalization ability.
Sources
Original: arXiv - cs.AI
