Qian Yiming
Harvard extension school, Harvard University, Boston, Massachusetts, United States of America.
PLoS One. 2025 Jan 13;20(1):e0316955. doi: 10.1371/journal.pone.0316955. eCollection 2025.
To address the limitations of existing stock price prediction models in handling real-time data streams-such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices-this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model's adaptability to dynamic changes. To ensure that the model retains prior knowledge while integrating new information, a time series elastic weight consolidation (TSEWC) algorithm is introduced to enable efficient incremental training with incoming data. Experiments conducted on five publicly available datasets demonstrate that the proposed method not only effectively captures the temporal information in the data but also fully exploits the correlations among multi-dimensional features, significantly improving stock price prediction accuracy. Notably, the method shows robust performance in coping with non-stationary and frequently changing financial market data.
为了解决现有股价预测模型在处理实时数据流方面的局限性，如扩展性差、由于数据分布的动态变化导致预测性能下降以及难以准确预测非平稳股价等问题，本文提出了一种基于增量学习的增强型Transformer框架（IL-ETransformer）用于在线股价预测。该方法利用多头自注意力机制深入探索股价与特征因素之间复杂的时间依赖关系。此外，采用连续归一化机制来稳定数据流，增强模型对动态变化的适应性。为确保模型在整合新信息时保留先验知识，引入了时间序列弹性权重巩固（TSEWC）算法，以实现对输入数据的高效增量训练。在五个公开可用数据集上进行的实验表明，所提出的方法不仅有效地捕捉了数据中的时间信息，还充分利用了多维度特征之间的相关性，显著提高了股价预测准确性。值得注意的是，该方法在应对非平稳且频繁变化的金融市场数据时表现出强大的性能。
