Self-Supervised Representation Learning for Financial Time-Series Forecasting
Abstract
The traditional paradigm of supervised learning in financial time-series forecasting is increasingly challenged by the scarcity of high-quality labeled data, the non-stationary nature of global markets, and the inherent noise within price signals. This paper explores the transition toward self-supervised representation learning as a transformative framework for decoding the latent structures of financial systems. By utilizing pretext tasks such as contrastive learning, temporal shuffling, and masked reconstruction, self-supervised models can extract robust features from vast quantities of unlabeled market data, significantly improving the generalization of downstream forecasting tasks. We conduct a system-level analysis of these architectures, focusing on the structural trade-offs between computational efficiency and representational depth. The discussion extends beyond mathematical optimization to address the socio-technical dimensions of deployment, including the physical infrastructure required for large-scale pre-training, the governance challenges of black-box representations in regulated environments, and the systemic risks of algorithmic convergence. Furthermore, this research examines the ethical imperatives of fairness and sustainability, arguing that the energy-intensive nature of self-supervised learning must be balanced with its potential to enhance market stability and information efficiency. By synthesizing perspectives from systems engineering, artificial intelligence, and financial policy, this paper provides a comprehensive roadmap for the next generation of resilient and interpretable financial AI infrastructures.
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