A seasonal grey model with three parameter-interval grey numbers for forecasting natural gas production

Purpose The natural gas production exhibits seasonal oscillations, nonlinear increments and uncertain volatility. Three-parameter interval grey numbers not only contain the mean information but also reflect the uncertainty fluctuation range of the index. To forecast natural gas production, a matrixed Fourier grey Bernoulli model (MFGBM(1,1)) is proposed. Design/methodology/approach To reduce the seasonal volatility of the series, seasonal factors are incorporated into the weighted accumulative generation operator. Additionally, the Fourier correction term and the regular term are introduced into the grey Bernoulli model to further enhance the model’s adaptability to seasonally oscillating series. The grey wolf optimization algorithm is improved based on the convergence factor of the sigmoid function and the Gaussian stochastic wandering strategy. This improved algorithm is used to optimize the model parameters. Findings The accuracy of MFGBM(1,1) is verified by two natural gas-related cases and the model comparison experiment. The natural gas production in China is predicted and analyzed for the four quarters of 2025. The prediction shows that it will grow steadily over the next four seasons. Originality/value The natural gas production has obvious nonlinear characteristics and seasonal oscillations. Therefore, for the data characteristics of natural gas, a three-parameter interval grey number prediction model for natural gas forecasting is proposed.