Prediction of fertilizer price based on bidirectional LSTM

Abstract

As an important agricultural product, the market price of chemical fertilizer is regulated by various factors such as raw material price, exchange rate policy, supply, etc., and the fluctuation of chemical fertilizer price has a significant impact on the national economy. It can be seen that if the price of chemical fertilizer can be accurately predicted, its impact on agriculture and even the national economy can be minimized. Therefore, in order to accurately predict the price of fertilizers, this paper proposes a bidirectional long-short term memory neural network model based on the attention mechanism(A-Bi-LSTM). The model makes full use of the contextual relationship between the forward and backward directions on the time series, which can effectively adopt the long distance information for the sequence data relying on. Combined with the trend data of fertilizer transaction prices in recent years, this paper performs regression fitting on the data to generate a training model which is used to predict the price of chemical fertilizer. Finally, the root mean square error on the test set is 0.011, and good prediction results are obtained.