Financial time series forecasting faces challenges such as market uncertainty, high-frequency data explosion, nonlinear relationships, and multi-source heterogeneous data fusion. Traditional statistical methods such as ARIMA and GARCH have limitations in complex environments, while machine learning/deep learning can capture nonlinear relationships, but face issues of insufficient sensitivity and interpretability in feature engineering.This study integrates multi-source heterogeneous data (historical fluctuations, market sentiment, macroeconomic indicators, etc.), combines BERT attention mechanism to capture text semantic correlations, uses BiLSTM bidirectional modeling capability to extract temporal dependent features, and constructs a transfer learning framework to achieve cross market/cyclical knowledge transfer; Introducing Hybrid Multiscale Decomposition (HMSD) to extract intrinsic features of time series, designing Interval Anomaly Detection and Large Scale Prediction Strategy (LSF), and quantifying prediction uncertainty through interval reliability analysis.ti-source data fusion and BERT mechanism significantly enhance information richness and model expression ability; The combination of multi-scale decomposition and BiLSTM enhances the ability of nonlinear dynamic modeling; Interval anomaly detection and LSF strategy solve uncertainty quantification problems, improving the robustness and interpretability of results; Transfer learning has validated its adaptability in cross market scenarios such as carbon trading and crude oil prices.Future improvements are needed in concept drift, real-time decomposition techniques, parameter optimization, and social network analysis; Explore dynamic update mechanisms to respond to market changes, introduce real-time decomposition to enhance high-frequency data response capabilities, optimize BERT BiLSTM architecture with automatic parameter tuning, integrate social network analysis to enhance multi market collaborative prediction capabilities, improve the robustness and generalization ability of the model in complex financial environments, and provide stronger methodological support for accurate volatility prediction and risk management.

Fang Li. Research on Multi Source Data Driven Financial Volatility Prediction Algorithm and Transfer Learning Framework Integrating BERT Attention and BiLSTM. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 99-108. https://doi.org/10.38007/SESR.2025.060210.

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