Prediction of equipment maintenance support capability of synthetic brigade based on time series mining

In view of the problem that there are too many parameters in the existing prediction method and the accuracy is not high, the time series mining method is used to predict the equipment maintenance support capability of the synthetic brigade in a certain period of time in the future. Firstly, the index system is established and the data of the equipment cloud platform is used to calculate the sequence of indicators and equipment maintenance support ability. Then, the segmentation fitting, clustering, symbolic expression and Apriori association mining are applied to the multivariate time series. The autoregressive integrated moving average-support vector regression (ARIMA-SVR) model and back propagation (BP) neural network are used to predict the equipment maintenance support capability of the synthetic brigade. Finally, the proposed method is verified by an example. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.