Combination prediction model of agricultural machinery equipment demand based on SARIMA-improved RS-multistep LSTM
In view of the fact that the demand for agricultural machinery equipment is affected by actual agricultural production and many other factors,and the demand data is cyclical and non-linear,making it difficult to accurately forecast the demand for agricultural machinery,a material demand forecasting method integrating SARIMA-improved RS-multistep LSTM was proposed.The seasonal differential autoregressive moving average(SARIMA)model was constructed by determining the parameter combination.The complete set Empirical Mode decomposition(CEEMDAN),improved random search(RS)algorithm and multistep short term memory network(LSTM)were introduced to construct an improved RS-Multistep LSTM model.The optimal weighted combination of SARIMA model and improved RS multistep LSTM model was used to obtain a combined prediction model.Using a certain model of agricultural machinery equipment as an example for verification.The results have showed that the proposed method can effectively predict the time series of the demand for agricultural machinery equipment,the evaluation indicators MSE,MAE and R2 are 225.45,13.22 and 0.920 9 respectively.
万方数据
维普
