一种基于LSTM物流资源需求预测模型

A LSTM-based prediction model for logistics resource demand

对云制造环境下物流资源需求进行精准预测的方法展开研究。首先,分析了传统预测方法的不足。其次,设计了一个基于长短时记忆(LSTM)预测模型,用于云制造环境下的物流资源需求预测。最后,通过对预测结果进行多方面的评价,可以发现相较于其他方法,文中模型在多因素环境下具有更高的预测精度和更佳预测效果,具有可行性。

In order to enhance the overall strength and competitiveness of logistics enterprises,it is necessary to effectively solve the changing needs of both supply and demand sides.Therefore,this paper will carry out research on the method of accurate prediction of logistics resource demand in cloud manufacturing environment.First,this paper analyzes the shortcomings of traditional forecasting methods.Secondly,a prediction model based on long short-term memory(LSTM)is designed for the prediction of logistics resource demand in cloud manufacturing environment.Finally,by evaluating the prediction results in various aspects,it can be found that compared with other methods,the model in this paper has higher prediction accuracy and better prediction effect in a multi-factor environment,and is feasible and constructive.