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.

With the acquisition of sustainable development, pursuing the coordinated development of social economy and ecological environment has been a critical approach of Northeastern China. Since the reform and opening‐up, marketization has profoundly affected the regional social economy. However, what role will marketization play in regional sustainable development? Can marketization improve sustainable development measured by the coupling coordination degree? This paper adopts the coupling coordination degree model (CCDM) to estimate the coupling coordination degree and further explore the impact of marketization on the coupling coordination degree in Northeastern China from 1994 to 2019. The results show that the average values of the static coupling degree (SCD), dynamic coupling degree (DCD), and coupling coordination degree (CCD) remained in a tiny coordinated state, which indicated that the level of sustainable development in Northeastern China is presented still primary from 1990 to 2019. Marketization has a significant negative impact on the coupling coordination degree, which indicates that marketization should be considered in the process of sustainable development in Northeastern China.

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.

With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM‐Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM‐Elman model in time‐series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM‐Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user‐specified dimensions on the proposed FM‐Elman neural network.