Research on Intelligent Analysis Framework for Time Series Data and Financial Risk Early Warning in the Digital Economy

Abstract

With the rapid development of the digital economy, the scale and complexity of financial market transactions have grown exponentially. Traditional risk warning models based on static data are no longer able to meet the needs of dynamic risk identification. This study addresses the challenge of dynamic correlation analysis of time-series data and innovatively constructs an intelligent analysis framework that integrates spatiotemporal attention mechanism and time-series graph neural network, achieving unified modeling and real-time warning of market risk, credit risk, and liquidity risk. This framework captures key time nodes and risk transmission paths through spatiotemporal attention mechanisms, and combines time-series graph neural networks to analyze dynamic relationships between financial institutions, effectively solving the problem of information fragmentation in traditional methods for modeling multidimensional time-series data. The introduction of CRITIC objective weighting method to optimize multi model fusion strategy, combined with ARIMA-GARCH combination model and deep learning algorithm, significantly improved the cross scale correlation analysis ability between high-frequency trading data and macroeconomic indicators.

The research has broken through the limitations of traditional models at the methodological level, achieving accurate characterization of risk contagion networks through dynamic weight allocation and real-time correlation analysis. Real time test data shows that the response delay of the framework in abnormal transaction detection is reduced by 96% compared to traditional systems, the accuracy of risk event warning is 92.4%, and the warning signal is sent out on average 14.3 minutes in advance. In the bond default warning task, the AUC value of the model reached 0.91 in the 9 months before default, which was 0.14 higher than the traditional logistic regression model, and the recall rate increased to 87%, effectively reducing the risk of underreporting. The experiment of multi-source heterogeneous data fusion shows that after integrating satellite images and supply chain data, the KS value of the enterprise credit evaluation model increases by 45.2%, and the recognition accuracy of credit risk for manufacturing enterprises increases by 18.7%. The study further validated the robustness of spatiotemporal joint modeling in extreme market environments. In the 2022 global financial market volatility test, the framework successfully alerted regional bank liquidity crises 23 trading days in advance.

On a theoretical level, this study improves the spatiotemporal dynamic analysis paradigm of financial risk warning and proposes a risk evolution modeling method based on deep learning and graph network technology. On a practical level, the framework can be directly embedded into financial institution risk control systems through modular design and real-time data interfaces, achieving dynamic assessment and graded warning of risk signals. The empirical results show that institutions adopting this framework have improved their risk disposal response efficiency by 40%, and the average annual risk exposure reduction brought about by capital allocation optimization is 3% -5%. The research provides a solution with both progressiveness technology and operational feasibility for financial risk prevention and control in the digital economy era, and its methodological breakthrough provides an important reference for research in complex system modeling, multi-source data fusion and other fields.