基于EEMD-LMD-LSTM-LEC深度学习模型的短时物流需求预测

短时物流需求预测是智慧物流系统的重要组成部分.由于短时物流需求数据具有非平稳性、强随机性、局部突变、非线性等特征,精确预测较为困难.对此,考虑集成经验模态分解(EEMD)、局部均值分解(LMD)、长短期记忆网络(LSTM)以及考虑局部误差校正(LEC),提出用于短时物流需求预测的EEMD-LMD-LSTM-LEC深度学习模型.该预测模型分为两个阶段:第1阶段基于特征分解和特征提取,构建EEMD-LMD-LSTM模型,以降低非线性的原始短时物流需求不平稳及随机变化导致的预测误差;第2阶段构建局部误差校正模型,用于校正第1阶段的预测结果,以减少短时物流需求的局部突变带来的预测误差.实验结果表明,EEMD-LMD-LSTM-LEC短时物流需求预测模型在均方根误差、绝对误差均值、绝对误差百分比和校正决定系数方面,均优于其他11种对比模型,其中包括:数理统计模型-----ARIMA;浅层机器学习模型-----支持向量回归和BP神经网络;深度学习模型-----LSTM和卷积神经网络;组合模型——深度置信网络-LSTM、经验模态分解(EMD)-LSTM、EEMD-LSTM、LMD-LSTM、EMD-LMD-LSTM和EEMD-LMD-LSTM.

Short-term logistics demand forecasting based on EEMD-LMD-LSTM-LEC deep learning model

Short-term logistics demand forecasting is one of critical components of the smart logistics system. As short-term logistics demand data is non-stationary, nonlinear series with strong randomness and singular points, it is difficult to accurately predict short-term logistics demand. Therefore, this paper proposes EEMD-LMD-LSTM-LEC deep learning model for short-term logistics demand forecasting, based on ensemble empirical mode decomposition(EEMD), local mean decomposition(LMD), and long and short-term memory(LSTM) neural networks while considering local error correction(LEC). The proposed model is divided into two stages. In the first stage, the EEMD-LMD-LSTM model is constructed based on feature decomposition and feature extraction, to reduce the error caused by non-linearity, non-stationarity and randomness of short-term logistics demand. In the second stage, a local error correction model is constructed to adjust the prediction results in the first stage for reducing the error caused by the singular points of short-term logistics demand. The results show that the proposed EEMD-LMD-LSTM-LEC model works better than other eleven models, in terms of root mean square errors, mean absolute errors, mean absolute percentage errors and the adjusted coefficient of determination, including the mathematical statistics model---ARIMA, shallow machine learning models---support vector regression and the BP neural network, deep learning models---LSTM and the convolutional neural network, combined models---the deep belief network-LSTM, empirical mode decomposition(EMD)-LSTM, EEMD-LSTM, LMD-LSTM, EMD-LMD-LSTM and EEMD-LMD-LSTM.