Core Viewpoint - The article discusses the application of deep learning models, particularly LSTM, in predicting government bond yields, highlighting its advantages in handling complex financial time series data [4][5]. Group 1: Financial Time Series Prediction - Financial time series prediction has evolved through three main stages: traditional econometric models, traditional machine learning models, and deep learning models [4]. - Deep learning models, especially LSTM, are currently among the mainstream methods for financial time series prediction due to their ability to adapt to non-stationary, non-linear, high-noise, and long-memory characteristics [4]. Group 2: LSTM Model for Bond Yield Prediction - A three-layer stacked LSTM model with Dropout regularization was developed to predict the 10-year government bond yield, exploring the application and effectiveness of deep learning in fixed income quantitative analysis [5]. - The model utilized data from early 2021 to December 12, 2025, with approximately 130,000 adjustable parameters, achieving an average absolute error of 1.43 basis points in predictions [5]. - The model predicts a slight decline in the 10-year government bond yield, with a forecasted value of 1.8330% for December 19, 2025, down from 1.8396% on December 12, 2025 [5]. Group 3: Future Optimization Directions - Future optimizations include adjusting the model design regarding time windows, data processing, network architecture, and training strategies [6]. - Expanding input variables to include macroeconomic, market, and sentiment data will enhance the model's predictive accuracy and economic logic [6]. - Combining LSTM with traditional econometric models or other machine learning models to create hybrid models like ARIMAX-LSTM and CNN-LSTM-ATT can improve prediction precision [7].