发表机构
Indian Institute of Technology Jodhpur; FLAME University（印度理工学院焦特布尔分校; FLAME大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DSTNet提出因果动态谱轨迹与分解谱变换器，在金融多时域预测中超越所有基线，但未证实择时优势。
AI 中文摘要
基于小波的金融预测器通常仅使用变换进行去噪，或将其简化为预测原点处的单一谱快照，且生成系数的卷积通常是双向的，因此可以读取预测原点之后的数据。DSTNet则保留滤波器组幅值的近期演化作为因果动态谱轨迹，该轨迹由二十天回溯期内的七个滞后技术指标构建，采用单侧Morlet派生滤波器组，并对左边界瞬态进行显式预热。一个分解的尺度-时间谱变换器沿时间和滤波器组轴分别进行注意力处理，一个学习到的门控将谱分支与CNN-BiLSTM融合，时域特定的门控在单次前向传播中输出一天、三天、五天和十天的预测。我们在共同的扩展窗口协议和未触碰的一年留出集上，对七个股票指数和黄金进行了评估，并与九个学习基线及一个随机游走持久性基准进行比较。在MAE和MAPE指标下，持久性在32个序列-时域单元中的29个单元中是十个固定竞争者中最强的，而DSTNet是每个单元中唯一低于它的模型，在一天时域上低0.7%至0.9%，在十天时域上低3.4%至4.5%。在一天时域上，配对检验显示DSTNet优于较弱的已学习基线，但与持久性和最强的已学习预测器相比结果不显著。下游的资产配置诊断不支持七个指数中任何一个的择时优势。
英文摘要
Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients is usually bilateral, so it can read past the forecast origin. DSTNet instead retains the recent evolution of filter-bank magnitudes as a causal Dynamic Spectral Trajectory, built from seven trailing technical indicators over a twenty-day lookback with a one-sided Morlet-derived filter bank and an explicit burn-in for the left-boundary transient. A factorized Scale-Temporal Spectral Transformer attends along the time and filter-bank axes separately, a learned gate fuses the spectral branch with a CNN-BiLSTM, and horizon-specific gates emit one, three, five, and ten day forecasts in a single pass. We evaluate seven equity indices and gold under a common expanding-window protocol and an untouched one-year hold-out, against nine learned baselines and a random-walk persistence benchmark. Under MAE and MAPE, persistence is the strongest of the ten fixed competitors in 29 of the 32 series-horizon cells and DSTNet is the only model below it in every cell, by 0.7 to 0.9 percent at one day and 3.4 to 4.5 percent at ten days. At one day, paired testing favours DSTNet against the weaker learned baselines but is inconclusive against persistence and the strongest learned forecasters. A downstream allocation diagnostic does not support an equity-timing advantage on any of the seven indices.
Comments27 Pages, 8 figures, 19 tables, Paper in Review
