Security Monitoring Data Fusion Method Based on ARIMA and LS-SVM

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Xu, Kaiwen^1 ; Yu, Jin^1 ; Hu, Yanzhu^1 ; Ai, Xinbo^1

School of Automation, Beijing University of Posts and Telecommunications, Beijing

100876, China^1

关键词: Accident prediction;    Autoregressive integrated moving average models;    Combined forecasting;    Data fusion methods;    Least squares support vector machines;    Prediction accuracy;    Security monitoring;    Security supervisions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/4/042104/pdf
DOI  :  10.1088/1755-1315/252/4/042104

来源: IOP

【 摘 要 】

Using the Autoregressive Integrated Moving Average Model (ARIMA) and least squares support vector machine model (LS-SVM), the data of the security monitoring data obtained during the security supervision process is data fusion, and the data is reduced by data. After the components are analyzed, the accident prediction is performed based on the improvement of data processing efficiency. Finally, the main data analysis (PCA) of the 15-dimensional data is used to reduce the dimension to the 7-dimensional data based on the accuracy of the information. After that, the data fusion technology is used to fuse the data to establish the ARIMA-LS-SVM combination. The model uses the combined forecasting model to predict and analyze the safety production accidents, and uses the actual data to verify. The results show that the data fusion technology can improve the efficiency of data processing. The model fits the time series of safety accidents well. The high prediction accuracy can help the company's safety production accident prediction in the future.