Multivariable logistics freight volume prediction method based on LSTM network

A forecasting method and freight volume technology, applied in the field of multi-variable logistics freight volume forecasting based on LSTM network, can solve problems such as slow convergence speed, weak nonlinear approximation ability, generalization ability constraints, etc., to achieve simplified implementation and simplified processing The effect of predicting and guaranteeing the convergence speed

Inactive Publication Date: 2020-08-21
HOHAI UNIV +1
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Problems solved by technology

Traditional artificial neural network methods often need to extract artificial feature parameters, which requires strong domain knowledge and experience, and shallow machine learning has limited ability to represent complex functions in the case of limited samples. The ability to transform is subject to certain restrictions, and the

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  • Multivariable logistics freight volume prediction method based on LSTM network
  • Multivariable logistics freight volume prediction method based on LSTM network
  • Multivariable logistics freight volume prediction method based on LSTM network

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Embodiment Construction

[0056] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0057] The following is a detailed description based on a case from a certain country in West Africa. It should be pointed out that when this method is applied to countries or regions with different land use characteristics, only the data sources are different, that is, the databases used are different, and the training of the model is consistent with the prediction method. In addition, the described embodiments are only intended to facilitate the understanding of the present invention, and do not limit any practical application.

[0058] A kind of LSTM network-based multi-variable logistics cargo volume prediction method according to the present invention, the realization process is as follows figure 1 shown, including the following steps:

[0059] (1) Analysis and screening of influencing factors

[0060] According to the...

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Abstract

The invention discloses a multivariable logistics freight volume prediction method based on an LSTM network, which is used for solving the technical problem of low prediction precision in time seriesdata prediction in the prior art. The method comprises the following steps of: screening logistics freight volume influence factors and preprocessing influence factor data; converting a time series data set supervised learning mode; normalizing the time series data variables of the supervised learning format; dividing a data training set and a test set; setting parameters of an LSTM prediction model and carrying out forward training on the model; and performing back propagation of the model and back normalization of a logistics freight volume prediction value. According to the method, the long-term memorability of the LSTM network for the flow data is fully utilized, the relation between variables can be effectively explored through supervised learning, and the logistics freight volume prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning and data prediction, and in particular relates to a multivariable logistics cargo volume prediction method based on an LSTM network. Background technique [0002] The forecast of logistics freight volume is one of the important basis for determining the development scale and logistics capacity level of logistics facilities in the region, planning the overall layout of logistics development in the region, and dividing the functions of logistics centers in the region. It is the primary preliminary work for regional logistics planning and decision-making. [0003] The forecast of logistics freight volume is to collect the historical demand data of logistics in the region, analyze the relationship between the change of logistics freight volume and various influencing factors, and use the influencing factors that can fully reflect the change trend of logistics freight volume to predict it. The...

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Application Information

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IPC IPC(8): G06Q10/08G06N3/04G06N3/08G06Q10/04G06Q10/06
CPCG06N3/084G06Q10/06393G06Q10/04G06Q10/0838G06N3/044
Inventor 郑长江邓夕贵赵孝进杨涛杜牧青王荣封学军谢守鹏雷智鹢翁志伟蒋柳鹏陈亮王兆恒
Owner HOHAI UNIV
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