Ensemble learning mine pressure prediction method based on variable time series shift Transformer-LSTM

The existing mine pressure prediction models are mostly single prediction models that rely on fixed length time series features. It is difficult to accurately capture the composite features of mine pressure time series data, which affects the accuracy of mine pressure prediction. To solve this problem, an ensemble learning mine pressure prediction method based on variable time series shift Transformer-long short-time memory (LSTM) is proposed. Based on the Laida criterion and Lagrange polynomial method, the outlier values in the mine pressure monitoring data are eliminated, and the missing values are inserted. Then normalized preprocessing is performed. The paper proposes a variable time series shift strategy to divide mine pressure time series data at different scales. It avoids potential data shift issues that may exist in fixed length time series. On this basis, the ensemble learning mine pressure prediction model based on Transformer-LSTM is constructed. By combining the attention mechanism and the accurate time series feature representation capability, the dynamic features of the mine pressure change law are captured at multiple levels. The voting algorithm of ensemble learning is used to jointly predict the mine pressure data to overcome the limitations of a single prediction model. The experimental results show that the voting algorithm of ensemble learning can reduce the volatility of mean absolute error (MAE) and root mean square error (RMSE) of mine pressure prediction. It effectively reduces the sensitivity impact of different scale feature series to the mine pressure prediction results. Compared with the Transformer model, the MAE of the Transformer-LSTM model's prediction results on two roof mine pressure datasets of fully mechanized working faces improves by 8.9% and 9.5% respectively, and the RMSE has increased by 12.7% and 16.5% respectively. The above indexes are also higher than those of back propagation (BP) neural network model and LSTM model. The method proposed in the paper effectively improves the accuracy of mine pressure prediction.