用时间序列Transformer预测银行稳定性，效果优于传统模型。
Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models
- 基于自注意力机制捕捉金融数据的复杂时序依赖关系。
- 在MSE和MAE上均优于LSTM、GRU等模型，误差更低。
- 适合金融风控、量化分析人员参考，提升风险预警能力。
本文研究基于时间序列Transformer模型的银行稳定性指数预测。银行稳定性指数是衡量金融机构健康状况与抗风险能力的重要指标。传统方法依赖单一宏观经济数据，难以适应复杂市场变化。本文提出一种基于时间序列Transformer的预测框架，利用模型的自注意力机制捕捉金融数据中的复杂时序依赖与非线性关系。通过实验对比LSTM、GRU、CNN、TCN及RNN-Transformer模型，结果表明时间序列Transformer在均方误差（MSE）和平均绝对误差（MAE）两项指标上均表现更优，展现出强大的预测能力。这说明该模型能更好处理银行稳定性预测中的多维时序数据，为金融风险管理提供新的技术路径与解决方案。
原文摘要 · Abstract (English)
This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional macroeconomic data. This paper proposes a prediction framework based on the Time Series Transformer, which uses the self-attention mechanism of the model to capture the complex temporal dependencies and nonlinear relationships in financial data. Through experiments, we compare the model with LSTM, GRU, CNN, TCN and RNN-Transformer models. The experimental results show that the Time Series Transformer model outperforms other models in both mean square error (MSE) and mean absolute error (MAE) evaluation indicators, showing strong prediction ability. This shows that the Time Series Transformer model can better handle multidimensional time series data in bank stability prediction, providing new technical approaches and solutions for financial risk management.
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