Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment

Introduction

Financial risk, as a core issue in the field of finance, has always garnered widespread attention. The instability and risks in financial markets have significant impacts on the stability and sustainable development of the global economic system (Cao et al., 2022). Therefore, studying the nature of financial risks, and methods to address these risks, becomes crucial. The uncertainty and volatility in financial markets makes it difficult for investors and decision-makers to predict and manage potential risks, further increasing the complexity of financial markets (Ahmed et al., 2022). Instability and risks often manifest in the form of sharp price fluctuations, increasing credit risks, and outbreaks of liquidity issues in the market. Market instability can be triggered by various factors, such as macroeconomic fluctuations, political events, natural disasters, and more. Credit risk involves the inability of corporations and governments to repay debt on time, potentially causing losses for financial institutions and investors. Additionally, liquidity issues can lead to market trading inefficiencies, further adding to market uncertainty (Taghian et al., 2022). To better address the instability and risks in financial markets, traditional financial models and methods have proven to be less flexible and accurate. The rise of deep learning technology offers a new approach to solving this problem (Swathi et al., 2022). Deep learning can handle high-dimensional, nonlinear, and dynamic financial data, effectively uncovering patterns and correlations hidden within the data. This capability provides financial professionals with more accurate predictive tools, promising to improve the efficiency and effectiveness of financial risk management.

Multivariate Time Series (MTS) data is an important data type in the financial field, comprising collections of multiple time series, such as the prices, interest rates, and trading volumes of different financial assets (Chauhan & Lee, 2022). The uniqueness of MTS data lies in its ability to capture the spatiotemporal correlations among different financial variables, reflecting the complexity and diversity of financial markets. It is this multidimensional information that makes MTS data a crucial source for financial risk management and forecasting (Fu et al., 2022). The analysis of such data can assist in making informed decisions and predictions within information management systems. Specifically, anomaly detection in MTS involves identifying and revealing uncommon patterns or events, which is crucial for areas such as fraud prevention and predictive maintenance in finance. For instance, detecting anomalies in financial time series data can help identify fraudulent activities and mitigate financial risks, which is particularly important for financial institutions and investors. Furthermore, timely identification and handling of anomalies in the financial market can reduce potential losses and enhance the efficiency of capital (Jo & Kim, 2023).

Past research has predominantly focused on statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Principal Component Analysis (PCA) (Siwach & Mann, 2022). While these traditional approaches have laid the foundation for the analysis of multivariate time series data, to some extent, they also exhibit notable limitations. ARIMA models often rely on the assumption of linearity, making it challenging to capture complex nonlinear correlations and anomalous patterns; thereby, limiting their applicability in the financial domain. On the other hand, although PCA can reduce the dimensionality of data, it assumes orthogonality among principal components, potentially overlooking underlying correlations in multivariate time series data and resulting in information loss. Alternatively, some studies have explored the use of clustering analysis to handle multivariate time series data, aiming to discover similarities and differences among different assets or time periods (J. Yao et al., 2022). The advantage of clustering analysis lies in its ability to identify potential market patterns and behaviors, offering better decision support for investors. However, traditional clustering methods often require pre-determining the number of clusters and sample distance measures, introducing subjectivity and limitations to the results.