Log In Sign Up

Intelligent Trading System: Multidimensional financial time series clustering

by   Pei Dehao, et al.

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.


page 5

page 13

page 15

page 18

page 19


Causal Analysis of Generic Time Series Data Applied for Market Prediction

We explore the applicability of the causal analysis based on temporally ...

Financial Trading Decisions based on Deep Fuzzy Self-Organizing Map

The volatility features of financial data would considerably change in d...

Clustering Time Series Data through Autoencoder-based Deep Learning Models

Machine learning and in particular deep learning algorithms are the emer...

Trading via Image Classification

The art of systematic financial trading evolved with an array of approac...

Generating virtual scenarios of multivariate financial data for quantitative trading applications

In this paper, we present a novel approach to the generation of virtual ...

Optimal Segmented Linear Regression for Financial Time Series Segmentation

Given a financial time series data, one of the most fundamental and inte...

Denoised Labels for Financial Time-Series Data via Self-Supervised Learning

The introduction of electronic trading platforms effectively changed the...