Intelligent Trading System: Multidimensional financial time series clustering

TL;DR

This paper introduces a novel multidimensional financial time series clustering approach using graph attention autoencoders and mask self-organizing maps, enabling better market behavior analysis and price prediction.

Contribution

It proposes a new clustering model combining GATE and Mask-SOM with Riemannian manifolds for analyzing noisy financial data, advancing market analysis techniques.

Findings

Effective clustering of multidimensional financial data.

Improved multi-step financial derivatives prediction.

Validated on real financial datasets.

Abstract

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.