论文标题
智能交易系统：多维财务时间序列集群
Intelligent Trading System: Multidimensional financial time series clustering
论文作者
论文摘要
多维时间序列聚类是时间序列数据分析中的重要问题。本文使用金融市场同一部分之间的交易之间存在的固有相关性为金融市场的行为分析提供了新的研究思想，以聚类和分析多维时序数据，以获取不同类型的市场特征。在本文中，我们提出了一个基于图形注意自动编码器（GATE）和MASK自组织地图（Mask-SOM）的多维时间序列聚类模型，我们基于该模型，我们实现了金融衍生品价格和智能交易系统构建的多步骤预测。为了获得并充分利用包含高噪声的多维财务时间序列数据之间的相关特征，在图形注意自动编码器中引入了恒定的曲率riemannian歧管，并将编码器捕获的多维财务时间序列特征嵌入了该份中。随后，使用Mask-SOM分析歧管编码实现了多维财务时间序列群集分析。最后，使用实际财务数据集验证了模型的可行性和有效性。
Multidimensional time series clustering is an important problem in time series data analysis. This paper provides a new research idea for the behavioral analysis of financial markets, using the intrinsic correlation existing between transactions in the same segment of the financial market to cluster and analyze multidimensional time-series data, so as to obtain different types of market characteristics. In this paper, we propose a multidimensional time series clustering model based on graph attention autoencoder (GATE) and mask self-organizing map (Mask-SOM), based on which we realize multi-step prediction of financial derivatives prices and intelligent trading system construction. To obtain and fully utilize the correlation features between multidimensional financial time series data containing high noise for clustering analysis, constant curvature Riemannian manifolds are introduced in the graph attention autoencoder, and the multidimensional financial time series features captured by the encoder are embedded into the manifold. Following that, the multidimensional financial time series clustering analysis is implemented using Mask-SOM analysis manifold encoding. Finally, the feasibility and effectiveness of the model are verified using real financial datasets.
