Coal mine dust concentration prediction method based on multivariate time series

To enhance the accuracy of dust concentration prediction in coal mines, a prediction method of coal mine dust concentration based on multivariate time series analysis was proposed according to the time series characteristics of coal mine dust concentration data. The method began by employing variational mode decomposition (VMD) to decompose the dust concentration time series signal into three components: trend, periodic, and random fluctuations. Subsequently, each component was predicted using different models: the grey prediction model (GM(1, 1)), the Holt-Winters triple exponential smoothing method, and the autoregressive moving average (ARMA(p, q)) model. The prediction results from these models were then combined to produce the final forecast value. The proposed method of dust concentration prediction was verified by using the existing mine monitoring data. Experimental results indicate that the method based on multivariate time series achieves an average absolute error (MAE) of 0.009 4, a mean square error (MSE) of 0.000 1, a root mean square error (RMSE) of 0.010 4, and a maximum relative error of 0.48%. Compared with the classical single and fused method, the dust concentration prediction method based on multivariate time series is superior to the classical method in MSE, RMSE and maximum relative error, which verifies the effectiveness of the method.