Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model

Abstract

Changes in shale gas production directly determine natural gas output in the United States (U.S.), and indirectly impact the global gas market. To better forecast shale gas output, we hybridized a nonlinear model with a linear model to develop a metabolic nonlinear grey model–autoregressive integrated moving average model (or MNGM-ARIMA). The proposed hybrid forecasting technique uses a linear model to correct nonlinear predictions, which effectively integrates the advantages of linear and nonlinear models and mitigates their limitations. Based on existing U.S. monthly shale gas output data, we applied the proposed hybrid technique to forecast U.S. monthly shale gas output. The results show that the proposed MNGM-ARIMA technique can produce a reliable forecasting results, with a mean absolute percent error of 2.396%. Then, using the same set of data, we also ran three other forecasting techniques developed by former researchers: the metabolic grey model (MGM), ARIMA, and non-linear grey model (NGM). The results of the comparison show that the proposed MNGM-ARIMA technique has the smallest mean absolute percent error. This indicates the proposed hybrid technique can produce more accurate forecasting results. We therefore conclude that the proposed MNGM-ARIMA technique can service us better forecasting shale gas output, as well as other fuels output.