The Statistical Significance of the Inclusion of Graph Neural Networks in the Financial Time Series Forecasting Problem
For researchers in financial time series forecasting, the paper demonstrates that geometric patterns can enhance prediction accuracy, though the improvement is incremental.
The paper investigates whether incorporating geometric patterns via Graph Neural Networks improves univariate financial time series forecasting. Results show statistically significant accuracy gains over temporal-only models.
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.