Arshia Abolghasemi, Seyed Reza Tavakoli, Parsa Naderi, Nazanin Mirzaei, Behrad Jahani, Saba Yousefzadeh, Tahereh Javaheri, Mohammad Hosein Rohban · 2026 · arXiv
DOI: 10.48550/arXiv.2610.21081 · open-access PDF
基于得分的散布方法通常会扰乱数据的Brownian Gaussian噪音,相当于一个轻尾前的斯托加斯特差异方程(SDE)。对于市场时间系列,这个SDE可能是一个糟糕的抽象:限量订单(LOB)订单流量是重尾,爆炸,有时跳跃类似。我们研究STOCHLOB,一个仅限于训练的分数匹配框架,它改变了前向散布腐败法,同时留下了部署的预测器。
Machine translation (Meta M2M100); the English original follows.
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time series this SDE can be a poor abstraction: limit order book (LOB) order flow is heavy-tailed, bursty, and occasionally jump-like. We study STOCHLOB, a training-only score-matching framework that changes the forward diffusion corruption law while leaving the deployed predictor deterministic. Instead of Brownian-only Gaussian corruption, the noising process uses two non-Gaussian SDE-inspired alternatives: jump-Lévy noise
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