Fertilizer sales forecasting model based on transformer-BiGRU
Enquan Cui, Shaomin Mu, Min Hou, Hongwei Lyu, Aiju Shi · 2023
Forecasting company's future fertilizer sales based on fertilizer sales data are important for reducing production costs for fertilizer companies. Fertilizer sales data have the feature of contextual time-series correlation, and the information of time-series correlation is not considered in the time series (fertilizer price, sales volume, etc.) prediction, which leads to low prediction accuracy. To address the above problems, a Transformer-BiGRU model is pro-posed in this paper. Transformer consists of a multi-headed self-attention, which is capable of mining the multi-feature dependencies between data, and is therefore chosen for multi-feature extraction of fertilizer sales data. However, parameters of transformer are passed in one direction, which cannot extract the temporal information well. Therefore, Bi-directional Gated Recurrent Unit (BiGRU) is introduced to extract the temporal correlation of fertilizer sales data by using its own forward and backward propagation to solve the former problem of poor ex-traction of contextual temporal information. The experimental results show that the Transformer-BiGRU model has the advantage of extracting the temporal correlation of multi-feature data and improves the accuracy of fertilizer sales prediction.