研究金融图像融合的可靠性，发现晚融合更稳，早融合易受噪声干扰。
When Fusion Helps and When It Breaks: View-Aligned Robustness in Same-Source Financial Imaging
- 用双视角图像（行情图+技术指标）做金融方向预测
- 晚融合在噪声环境下表现更稳定，早融合可能负向迁移
- 适合关注金融时序图像鲁棒性的研究人员
本文研究基于同一时间序列生成的两个确定性图像视图——OHLCV图表（ohlcv）与技术指标矩阵（indic）——在同源多视图学习中的应用，用于次日方向预测。为消除近零波动带来的标签模糊，采用事后最小移动阈值（min_move, tau），仅在真实绝对次日收益率不低于tau的子集上建立离线基准。在防泄漏的时间块划分（embargo）下，比较通道堆叠的早期融合与双编码器晚融合（可选跨分支一致性）。进一步评估像素空间L-infinity攻击（FGSM/PGD）在视图受限与联合威胁模型下的鲁棒性。结果表明：融合策略具有情境依赖性；在噪声较大时，早期融合可能出现负向迁移，而晚融合在标签稳定后更为可靠。鲁棒性在极小扰动预算下急剧下降，且存在视图依赖的脆弱性；晚融合在视图受限攻击中常有帮助，但联合扰动仍具挑战。
原文摘要 · Abstract (English)
We study same-source multi-view learning and adversarial robustness for next-day direction prediction using two deterministic, window-aligned image views derived from the same time series: an OHLCV-rendered chart (ohlcv) and a technical-indicator matrix (indic). To control label ambiguity from near-zero moves, we use an ex-post minimum-movement threshold min_move (tau) based on realized absolute next-day return, defining an offline benchmark on the subset where the absolute next-day return is at least tau. Under leakage-resistant time-block splits with embargo, we compare early fusion (channel stacking) and dual-encoder late fusion with optional cross-branch consistency. We then evaluate pixel-space L-infinity evasion attacks (FGSM/PGD) under view-constrained and joint threat models. We find that fusion is regime dependent: early fusion can suffer negative transfer under noisier settings, whereas late fusion is a more reliable default once labels stabilize. Robustness degrades sharply under tiny budgets with stable view-dependent vulnerabilities; late fusion often helps under view-constrained attacks, but joint perturbations remain challenging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。