基于CNN-LSTM-AM神经网络的多维长序列物流需求预测

Multi-dimensional Long Sequence Logistics Demand Forecasting Based on CNN-LSTM-AM Neural Network

物流需求预测是物流管理中的关键环节,但是在现实生活中,物流需求可能受到诸如天气、经济状况、特殊事件等多方面因素的影响,这使得问题呈现出多维度、长序列的特征。随着深度学习和神经网络的发展,越来越多的研究开始尝试使用神经网络模型进行物流需求预测,但是单一的神经网络模型在处理多维度、长时间序列的预测任务时常常表现欠佳。由此文章提出了一种基于CNN-LSTM-AM的神经网络模型,用于多维长序列物流需求预测。通过消融实验与其他模型的对比,结果表明,其平均绝对误差(MAE)、均方根误差(RMSE)、决定系数(R2)均值分别为1.56、1.63和0.981,均优于其他6种神经网络模型,为物流企业提供了一个有效的参考来更好地规划资源和降低成本。

Logistics demand forecasting is a critical component of logistics management,but in real life,logistics demand can be influenced by a variety of factors such as weather,economic conditions,and special events,presenting characteristics of multi-dimensionality and long sequences.With the development of deep learning and neural networks,more and more studies have begun to use neural network models for logistics demand forecasting.However,single neural network model often underperforms in handling multi-dimensional,long-time series forecasting tasks.Therefore,this study proposes a CNN-LSTM-AM based neural network model for multi-dimensional long sequence logistics demand forecasting.Compared with other models through ablation experiments,the results show that its Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Coefficient of Determination(R2)values are 1.56,1.63 and 0.981,respectively,all superior to six other neural network models.This provides an effective reference for logistics enterprises to better plan resources and reduce costs.