The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem

TL;DR

This paper demonstrates that incorporating geometric patterns via Graph Neural Networks significantly improves the accuracy of univariate financial time series forecasting, validated through extensive empirical evaluation.

Contribution

It introduces the Time-Geometric model that combines geometric and temporal pattern analysis, advancing univariate time series prediction methods.

Findings

Geometric patterns improve forecasting accuracy.

Graph Neural Networks provide statistically significant gains.

Extensive empirical evaluations support these conclusions.

Abstract

Forecasting univariate time series in the financial market is a challenging endeavor. While numerous statistical and machine learning models have been introduced to address this challenge, they typically concentrate solely on analyzing temporal patterns within the time series data. In this research, we study the statistical significance of the inclusion of geometric patterns in enhancing forecasting accuracy within the context of time series analysis. We introduce the Time-Geometric model, a combination of models designed to exploit both geometric and temporal patterns. The contribution of this research lies in advancing the domain of univariate time series prediction,as demonstrated through extensive empirical evaluations. Our findings underscore that leveraging geometric patterns, captured through Graph Neural Networks, yields statistically significant improvements in forecasting accuracy.