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Prediction of Gas Concentration Based on ARIMA and GARCH Model

机译：基于Arima和GARCH模型的气体浓度预测

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摘要

In order to accurately predict the dynamic gushing process of gas in fully mechanized mining face, based on the historical monitoring data of underground gas concentration, with the help of R language, the ARIMA model is first established and fitted to determine the prediction equation of the ARIMA (p, d, q). The results of data fitting show that the model has a high degree of fitting to the gas concentration time series. Then the GARCH (u, v) is applied to the residual sequence of ARIMA (p, d, q), and the predicted value of the noise term in the ARIMA model is simulated, and the prediction result of the gas emission concentration is optimized. Finally, the 1001 fully mechanized mining face of Huangling No. 1 Mine in Shaanxi Province is taken as an application example. The results show that the combined model of ARIMA (p, d, q) and GARCH (u, v) can not only reflect the change trend of gas emission concentration but also has a high fitting effect and prediction accuracy.
机译：为了准确预测气体的动态涌水过程，基于历史监测数据的地下气体浓度，借助于R语言，首先建立Arima模型，以确定预测方程Arima（p，d，q）。数据拟合结果表明，该模型对气体浓度时间序列具有高度的配合。然后将GARCH（U，V）应用于ARIMA（P，D，Q）的残余序列，并且模拟了ARIMA模型中的噪声术语的预测值，并且优化了气体发射浓度的预测结果。最后，陕西省黄岭1号矿井1001齐全机械化面对作为应用示例。结果表明，Arima（P，D，Q）和Garch（U，V）的组合模型不仅可以反映气体排放浓度的变化趋势，而且还具有高拟合效果和预测精度。

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