采用ARIMA模型与SARIMA模型对顶板一个断裂周期的液压支架载荷进

The ARIMA model and the SARIMA model are used to predict the load of the hydraulic support during one fracture cycle of the roof. Although both models have obtained the cycle characteristics of the hydraulic support, the prediction effect of the peak load and the change law of the hydraulic support is poor. The root mean square error (RMSE) was used to compare the fitting effects of the two models. The RMSE value of the ARIMA model was 5.62, and the RMSE value of the SARIMA model was 5.18. Both models failed to achieve hydraulic pressure for one fracture cycle of the roof. Support load is better predicted. The author also uses LSTM algorithm, RNN algorithm, etc. to model and analyze the data, but the effect of predicting the load of the hydraulic support for a roof fracture cycle is not ideal.

The ARIMA model and the SARIMA model were used to predict the hydraulic support load of a break cycle on the top plate, although both models obtained the cycle cycle change characteristics of the hydraulic support, but the prediction effect of the peak and variation law of the hydraulic support load was poor, and the fitting effect of the two models was compared with the RMSE. The RMSE value of the ARIMA model is 5.62 and the RMSE value of the SARIMA model is 5.18. The author also uses LSTM algorithm, RNN algorithm and so on to model and analyze the data, but the hydraulic support load prediction effect of a top plate break cycle is not ideal.

ARIMA model and SARIMA model are used to predict the load of hydraulic support in a fracture period. Although both models obtain the cyclic variation characteristics of hydraulic support, the prediction effect of peak value and variation law of hydraulic support load is poor. Root mean square error (RMSE) is used to compare the fitting effect of the two models. The RMSE value of ARIMA model is 5.62 The RMSE value of SARIMA model is 5.18. Neither of the two models can predict the load of hydraulic support in one fracture cycle of roof. The author also uses LSTM algorithm and RNN algorithm to model and analyze the data, but the prediction effect of hydraulic support load for a roof fracture period is not ideal.<br>