标题： 分布相关性感知的知识蒸馏用于股票交易量预测 显示英文标题

标题： Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

摘要： 传统知识蒸馏在分类问题中通过教师模型生成的软标签中的类别相关性来传递知识，而在股票交易量预测等回归问题中这些信息是不可用的。 为了弥补这一点，我们提出了一种新的蒸馏框架，用于在给定历史交易数据的情况下训练一个轻量级的学生模型来进行交易量预测。 具体来说，我们通过训练模型来预测交易量所属的高斯分布，将回归模型转化为概率预测模型。 因此，学生模型可以通过将其预测分布与教师模型的分布相匹配，在更具信息量的分布层面上从教师模型中学习。 进一步引入了两个相关性蒸馏目标，以鼓励学生模型与教师模型产生一致的成对关系。 我们在一个真实世界的股票成交量数据集上，采用两种不同的时间窗口设置对该框架进行了评估。 实验表明，我们的框架优于强大的基线模型，在压缩模型大小至$5\times$的同时保持了$99.6\%$的预测精度。 广泛的分析进一步表明，在资源受限的场景下，我们的框架比传统的蒸馏方法更为有效。 显示英文摘要

摘要： Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction. To remedy this, we present a novel distillation framework for training a light-weight student model to perform trading volume prediction given historical transaction data. Specifically, we turn the regression model into a probabilistic forecasting model, by training models to predict a Gaussian distribution to which the trading volume belongs. The student model can thus learn from the teacher at a more informative distributional level, by matching its predicted distributions to that of the teacher. Two correlational distillation objectives are further introduced to encourage the student to produce consistent pair-wise relationships with the teacher model. We evaluate the framework on a real-world stock volume dataset with two different time window settings. Experiments demonstrate that our framework is superior to strong baseline models, compressing the model size by $5\times$ while maintaining $99.6\%$ prediction accuracy. The extensive analysis further reveals that our framework is more effective than vanilla distillation methods under low-resource scenarios.