Multi-step prediction of ultra-short-term logistics demand based on VMD-EWT-QWLSTM-TPE deep learning model
Ultra-short-term logistics demand forecasting is important for intelligent scheduling of enterprise logistics resources.As ultra-short-term logistics demand data is random,highly volatile,and nonstationary,it is difficult to accurately predict them for multi-steps.Considering such characteristics,this research proposes a combination model for ultra-short-term logistics demand forecasting based on the serial data decomposition and quantum-weighted deep nerual network.Firstly,the time series features of the ultra-short-term logistics demand data are extracted with the decomposition method of serializing variational mode decomposition(VMD)and empirical wavelet transform(EWT)to strip the noise signal and reduce the non-stationarity and randomness of the original data.Secondly,a quantum-weighted long short-term memory neural network(QWLSTM)deep learning model is developed and a multi-input multi-output strategy is designed to predict the decomposed mode components in multi-steps,also the hyper-parameters of the QWLSTM are optimized by the Tree-Parzen-Estimator(TPE).Finally,the prediction results for each mode component are reconstructed.Numerous experiments are conducted,and the results show that the proposed model performs better than other 15 comparison models.
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