Time series data is common in data sets has become one of the focuses of current research. The prediction of time series can be realized through the mining of time series data, so that we can obtain the development process and regularity of social economic phenomena reflected by time series, and extrapolate to predict its development trend. More and more attention has been paid to time series prediction in the era of big data. It is the basic application of time series prediction to accurately predict the trend. In this paper, we introduce various time series autoregressive (AR) model, moving average (MA) model, and ARIMA model that is combined by AR and MA. As the time series prediction in general scenarios, the ARIMA is applied to the risk prediction of the National SME Stock Trading (New Third Board) in combination with specific scenarios. The case studies show that the results of our analysis are basically consistent with the actual situation, which has greatly helped the prediction of financial risks.

In order to implement the requirements of keeping the bottom line of systemic financial risks and preventing the mitigation of major risks, this work focuses on the investigation of multi-source heterogeneous data fusion algorithms and cleaning technologies to establish a suitable style for data analysis and big data computation frame. In this paper, according to the above method, we provide the basis for early analysis of economic security. Utilizing the big data analysis, an emerging information technology method, we can be able to explore new risk early-warning methods, build a risk monitoring and early-warning platform and achieve scientific economic decision-making, so that the sources of economic risk in national economic security can be traced.

Listed firms all over the globe are moving their focus from short‐term profit maximization to long‐term environmental, social, and governance (ESG) objectives. Most business leaders are now cognizant of the growing importance of ESG concerns, which may have an effect on an organization’s financial health as well as its reputation in the marketplace. A recent study found that countries with strong ESG performance may be able to improve their financial performance. Yet, in China, the subject of “how does ESG impact financial performance” has received little attention. In this paper, we examine the link between ESG operations and financial indices in China’s publicly traded firms using dynamic and static panel data analysis. We begin by gathering financial data and preprocessing it using z‐score normalization. The consequence of ESG variables on the financial performance of the company under the pandemic was investigated using statistical analytic techniques such as the Pearson correlation test, logistic regression model, and Fisher’s exact test. Due to the study’s dynamic and static data, comprehensive ESG has a considerable influence on corporate value and profitability per share. The impact of ESG variables on the financial performance of the company in the event of a pandemic was analyzed using analytical methods such as Pearson correlation, logistic regression, and Fisher’s exact test. Performance in ESG may boost financial performance, which could impact investors, business administrators, decision‐makers, and industry regulations.

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.