DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting
DSTNet is a deep learning model designed for causal multi-horizon financial forecasting. It innovates by capturing the dynamic evolution of spectral features over time using a Dynamic Spectral Trajectory tensor. The model generates four future forecasts simultaneously, but its performance compared to simple random walk persistence suggests it does not currently offer a predictive edge.