This paper introduces a deep learning model that improves time series forecasting by learning compact, noise-resistant representations with minimal redundancy. Its key innovation—a non-contrastive self-supervised learning method that improves accuracy and stability—makes it especially useful in finance for predicting complex, interrelated trends like stock prices or economic indicators.