Anomaly Detection in Financial Time Series Based on Recurrent Neural Networks and Latent Variable Autoregressive Models

Financial time series data can yield different values depending on the accounting standards applied, and the selection of these standards is often subject to human intervention. This variability introduces challenges in analyzing financial time series data, particularly due to the diversity and complexity of feature distributions. To address these challenges, a specialized anomaly detection method has been proposed for domain generalization in financial time series data. This method aims to capture the unique patterns and representations inherent in financial sequences. The process begins by using the results obtained from a Recurrent Neural Network (RNN) as learned knowledge. This knowledge is then adapted to the marginal distribution in the feature space using a standard classifier. After this adaptation, a latent variable autoregressive model is applied to further refine the predictions, thereby enhancing the accuracy of the forecasts. To improve the precision of risk prediction, a latent variable autoregressive model is also constructed. This model is designed to capture the feature distribution within the financial time series data, allowing it to effectively identify potential financial risks. The experimental results indicate that this approach is feasible and holds promise for accurately forecasting financial trends and risks by effectively managing the complexities of feature distributions in financial data. This method not only enhances the predictive capability of the model but also provides a robust framework for understanding and mitigating financial risks.