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DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting

Aashish Bohra, Lokendra Vishwakarm · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.09654 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Wavelet 基于强劲的金融预测器通常只使用转换来消化,或者在预测起源时将其缩小为一个单一的光谱快照,而产生因子的值通常是双边的,所以它可以读取预测起源。 DSTNet 相反,保留了滤波器银行大小的最近进化作为因果动态光谱轨道,从七个跟踪技术指标构建起,在一个二十天的回顾下,任何一侧的 Morlet 衍生过滤器银行和一个明确的燃烧为左边的过渡者。 一个因子化 Scale-Temporal Spectral Transformer 在时间和单独的滤波器银行轴之间,一个学到的门将光谱分支与一个 CNN-BiLSTM 合并,但地平线特

Machine translation (Meta M2M100); the English original follows.

Abstract

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.

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Topics: Econometrics, Snapshot (computer storage), Computer science, Equity (law), Spectral analysis, Mathematics

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