ARIMA-SVM组合模型驱动下的瓦斯浓度预测研究

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

针对单一瓦斯预测模型挖掘矿井瓦斯浓度时间序列全部特征能力较弱的问题,提出了一种基于自回归滑动平均模型(ARIMA)和支持向量机(SVM)模型的组合预测模型,并采用该模型对瓦斯浓度进行预测。首先,分别应用ARIMA模型和SVM模型对实验数据进行预测分析,得到2种单一模型预测结果。其次,结合自相关函数和偏自相关函数及贝叶斯准则,得到最优ARIMA模型为ARIMA(1,1,2),通过核函数等参数寻优,确立最优SVM模型,从而建立ARIMA-SVM组合模型。利用ARIMA模型处理瓦斯浓度时间序列的历史数据,得到相应的线性预测结果和残差序列,利用SVM模型进一步对数据残差序列中的非线性因素进行分析,得到非线性预测结果,将2个模型的预测结果进行组合,得到目标瓦斯时间序列最终预测结果。实验结果表明:① ARIMA-SVM组合模型预测结果与矿井实际数据的拟合度优于ARIMA模型和SVM模型。②相对于ARIMA模型、SVM模型,ARIMA-SVM组合模型的误差大幅度减小,且预测结果明显优于单一模型。③ ARIMA-SVM组合模型的平均绝对误差、平均绝对百分比误差及均方根误差均为最小,表明ARIMA-SVM组合模型预测精度更高。

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.