Unsupervised Temporal Encoding for Stock Price Prediction through Dual-Phase Learning

This paper proposes a two-stage self-supervised pretraining modeling method for stock price sequence prediction in financial markets. The method is designed to address challenges such as limited labeled data, complex structural patterns, and non-stationary temporal features. The framework consists of two phases: pretraining and fine-tuning. In the pretraining phase, two self-supervised tasks are constructed. One captures long-term trends, while the other models short-term fluctuations. In the fine-tuning phase, the learned representations are used for regression prediction to improve the model's ability to fit future price movements. In the encoder design, the method integrates multi-layer temporal sequence modeling units. This enables multi-granularity semantic extraction and structure-aware representation learning. For the experimental part, a dataset is built based on Tesla's historical stock data from 2010 to 2024. The model is systematically evaluated under different time windows, hidden dimensions, sampling frequencies, and perturbation settings. The experimental results show that the proposed method outperforms existing baseline models across multiple metrics. It effectively captures temporal dependencies while maintaining strong prediction stability and robustness. This study validates the effectiveness of the two-stage architecture in financial time series modeling. It also demonstrates the practical potential of self-supervised learning in low-supervision financial prediction tasks.