Context-Integrated Adversarial Learning for Predictive Modelling of Stock Price Dynamics

ArXi:2604.22801v1 Announce Type: cross It is a challenging task to forecast equity prices in fast moving financial markets as this becomes even difficult when the predictive signal is based on non-homogeneous information channels. The classical statistical methods, especially the Autoregressive Integrated Moving Average (ARIMA) models, limit their analytical ability with the linear assumptions that prevent the modeling of complex temporal dynamics.