Research on gas concentration prediction driven by ARIMA-SVM combined model

The single gas prediction model has weak capability in mining all characteristics of the mine gas concentration time sequence. In order to solve the problem, a combined prediction model based on autoregressive intergrated moving average (ARIMA) model and support vector machine (SVM) model is proposed. The model is used to predict gas concentration. Firstly, the prediction results of the two single models are obtained by using the ARIMA model and the SVM model to predict and analyze the experimental data respectively. Secondly, combining the autocorrelation function, partial autocorrelation function and Bayesian criterion, the optimal ARIMA model is obtained as ARIMA(1,1,2). According to the optimization of kernel function and other parameters, the optimal SVM model is established, and then the ARIMA-SVM combined model is established. The ARIMA model is used to process the historical data of the gas concentration time series and obtain the corresponding linear prediction result and the residual sequence. The SVM model is used to further analyze the nonlinear factors in the data residual sequence and obtain the unlinear prediction result. The prediction results of the two models are combined to obtain the final prediction result of the target gas concentration time series. The experimental results show the following results. ① The fitting degree of the prediction results of the ARIMA-SVM combined model is better than that of the ARIMA model and SVM single model. ② Compared with the ARIMA model and SVM model, the error of the ARIMA-SVM combined model is greatly reduced, and the prediction result is obviously better than that of the single model. ③ The mean absolute error, mean absolute percentage error and root mean square error of the ARIMA-SVM combined model are the smallest. This result indicates that the prediction precision of the ARIMA-SVM combined model is higher.