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Short-time logistics demand prediction method, apparatus and device, and readable storage medium

A technology for logistics demand and demand forecasting, applied in the field of equipment and readable storage media, devices, and short-term logistics demand forecasting methods, can solve the problems of hysteresis of forecast results, difficult to guarantee accuracy, and non-stationary logistics data, so as to improve forecasting. Accuracy, Improved Accuracy, Reduced Effects of Endpoint Effects

Active Publication Date: 2021-06-18
SOUTHWEST JIAOTONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Scholars at home and abroad focus on logistics demand forecasting based on year as the statistical unit of logistics demand, and its data change trend is relatively stable. Its logistics data has the characteristics of non-stationary, strong randomness, and local mutation. Existing research methods directly apply short-term logistics demand forecasting, and the forecasting results have serious hysteresis, and the accuracy is difficult to guarantee. exhibits the characteristics of a random walk

Method used

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  • Short-time logistics demand prediction method, apparatus and device, and readable storage medium
  • Short-time logistics demand prediction method, apparatus and device, and readable storage medium
  • Short-time logistics demand prediction method, apparatus and device, and readable storage medium

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Embodiment 1

[0040] This embodiment provides a short-term logistics demand forecasting method.

[0041] see figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400 and step S500.

[0042] S100. Obtain first information. The first information includes historical logistics data within a first time period, and the first time period is a period of time with the current moment as the statistical end point;

[0043] It should be noted that in this embodiment, a total of 100,000 pieces of logistics demand data of an enterprise from July 1, 2019 to December 31, 2019 will be used to illustrate the data processing methods and data processing methods of some steps in this embodiment. Flow direction, the first time period in this embodiment is the period from July 1, 2019 to December 31, 2019, that is, the first information includes all logistics demand information in the first time period.

[0044] S200. Based on the first information, divide the first inform...

Embodiment 2

[0111] Such as image 3 As shown, this embodiment provides a short-term logistics demand forecasting device, including:

[0112] The first information acquiring unit 1 is configured to acquire first information, the first information includes historical logistics data within a first time period, and the first time period is a period of time with the current moment as the statistical end point;

[0113] The information segmentation unit 2 is used to divide the first information into multiple subsets based on the first information in units of a preset time period, count the actual logistics demand value in each subset, and select two subsets with the latest time period to form prediction set;

[0114] The training unit 3 is used to establish a short-term logistics demand forecasting model based on feature decomposition and feature extraction, use the sample set to train the short-term logistics demand forecasting model, and obtain the trained short-term logistics demand forecas...

Embodiment 3

[0143] Corresponding to the above method embodiment, this embodiment also provides a short-term logistics demand forecasting device, a logistics demand forecasting device described below and a short-term logistics demand forecasting method described above can be referred to each other .

[0144] Figure 4 It is a block diagram of a short-term logistics demand forecasting device 800 shown according to an exemplary embodiment. Such as Figure 4 As shown, the short-term logistics demand forecasting device 800 may include: a processor 801 and a memory 802 . The short-term logistics demand forecasting device 800 may also include one or more of a multimedia component 803 , an input / output (I / O) interface 804 , and a communication component 805 .

[0145] Wherein, the processor 801 is used to control the overall operation of the short-term logistics demand forecasting device 800, so as to complete all or part of the steps in the above-mentioned logistics demand forecasting method....

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Abstract

The invention provides a short-time logistics demand prediction method, device and equipment and a readable storage medium, and relates to the technical field of logistics demand prediction, and the method comprises the steps: obtaining first information, wherein the first information comprises feature information obtained after decomposition of historical logistics data in a first time period through EEMD and LMD, establishing an LSTM prediction model for logistics demand prediction, and correcting logistics demand prediction through an error correction mathematical model. According to the method, the EEMD-LMD-LSTM-LEC model is provided from two perspectives of feature decomposition and feature extraction aiming at the characteristics of non-stationarity, strong randomness, local mutation, nonlinearity and the like of short-time logistics data, so that the problems of relatively large prediction error and prediction hysteresis of direct prediction caused by non-linear unsteady original requirements are solved. And the prediction precision is improved.

Description

technical field [0001] The present invention relates to the technical field of short-term logistics demand forecasting, in particular to a short-term logistics demand forecasting method, device, equipment and readable storage medium. Background technique [0002] The research object of domestic and foreign scholars on logistics demand forecasting is mainly the logistics demand with year as the statistical unit, and its data change trend is relatively stable. Its logistics data has the characteristics of non-stationary, strong randomness, and local mutation. Existing research methods directly apply short-term logistics demand forecasting, and the forecast results have serious lag, and the accuracy is difficult to guarantee. It cannot cope with the logistics data in terms of quantity and time. exhibits the characteristics of a random walk. Contents of the invention [0003] The purpose of the present invention is to provide a short-term logistics demand forecasting method, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/08G06Q30/02G06Q10/06G06N3/08G06N3/04
CPCG06Q10/06315G06Q10/08G06Q30/0201G06Q30/0202G06N3/049G06N3/08G06N3/044
Inventor 陈彦如冉茂亮杨新彪
Owner SOUTHWEST JIAOTONG UNIV
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