发表机构
National University of Singapore; ETH Zürich; Centre for Quantum Technologies, National University of Singapore; Department of Information Technology and Electrical Engineering, ETH Zürich; School of Computing, National University of Singapore（新加坡国立大学; 苏黎世联邦理工学院; 新加坡国立大学量子技术中心; 苏黎世联邦理工学院信息技术与电气工程系; 新加坡国立大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出量子加权移动平均（QWMA）模型，结合经典预处理与酉矩阵线性组合层，在FI-2010及中国A股数据集上测试，虽未证量子优势，但性能接近最优经典模型，可聚焦时间序列关键预测部分。
AI 中文摘要
量子计算机能否用于预测多变量金融时间序列？本研究关注从限价订单簿（LOB）数据预测价格趋势的问题。在确定经典模型的关键组件后，我们引入量子加权移动平均（QWMA）模型，其两个主要构建模块为：一是对特征维度和时间维度进行归一化的经典预处理，二是基于酉矩阵线性组合的层，用于处理酉嵌入的经典数据。该模型在FI-2010基准数据集和中国A股股票的第二个数据集上进行评估。虽然我们未提供量子优势的证据，但该经典-量子混合模型的性能接近最佳经典模型；模型的量子部分具备足够表达能力，可聚焦于时间序列中最具预测性的部分。我们还考虑了QWMA模型的多种特化形式，尤其是与广泛使用的指数移动平均（EMA）相关的变体，并探讨了方法与数据集的局限性。
英文摘要
Can quantum computers be useful for forecasting multivariate financial time series? In this work, we consider the problem of predicting price trends from limit order book (LOB) data. After identifying key components of classical models, we introduce the quantum weighted moving average (QWMA) model. The two main building blocks are, first, classically preprocessing via normalization of both feature and temporal dimensions and, second, a linear combination of unitaries-based layer for unitarily-embedded classical data. The models are evaluated on the FI-2010 benchmark dataset and a second dataset of China A-share stocks. While we do not present evidence of quantum advantage, the combined classical-quantum model demonstrates performances close to the best classical models. The quantum part of the model is expressive enough to focus on the most predictive parts of the time series. Several specializations of the QWMA model are considered, in particular a variant related to the widely-used exponential moving average (EMA). We give consideration to the limitations of the methods and the datasets.
Comments30 pages, 17 figures
