Methods for forecasting coal production in China

Coal production forecasting contributes to the development of scientifically grounded production plans for coal enterprises. Optimizing resource allocation and minimizing operational risks help strengthen market positions in a highly competitive environment and ensure the stable growth of companies. Historical data on coal production in China from 2003 to 2023 were selected to establish the forecasting database. The forecasting methods employed include a regression forecasting model, a grey forecasting model, an exponential smoothing model, and the ARIMA (Autoregressive Integrated Moving Average) model. A comparative analysis of the accuracy of these methods is conducted. The integrated research results provide a forecast interval for coal production in China from 2024 to 2026. The analysis indicates that the exponential smoothing model and the ARIMA model demonstrate superior fitting performance.

Authors: D. A. Pervukhin, Tan Lisha, O. V. Afanaseva

Direction: Informatics, Computer Technologies And Control

Keywords: multivariate data, linear regression, grey forecasting, ARIMA model, coal production

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