|Table of Contents|

Logistics demand chaotic prediction model by error compensation(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年01期
Page:
126-
Research Field:
Publishing date:

Info

Title:
Logistics demand chaotic prediction model by error compensation
Author(s):
Xu Qin
School of Business and Logistics,Wuhan Business University,Wuhan 430056,China
Keywords:
logistics demand error compensation chaos theory learning samples least squares support vector machine
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2018.42.01.019
Abstract:
Research on logistics demand prediction can provide valuable reference information for scientific planning of logistics park. Aiming at high prediction error of single logistics prediction models and get better prediction results,a novel logistics demand prediction model based on error compensation is designed. Firstly,historical data of logistics demand prediction are dealt by chaos theory to data the change rule and establish a sample of logistics demand prediction; secondly least squares support vector machine is used to model and predict learning samples while an error prediction model is established by using auto regressive moving average to estimate the residual sequence of least squares support vector machine predicting results; at last,the logistics demand prediction result is error compensation,and the performance is tested. The results show that it can effectively reduce error of logistics demand prediction,obviously improve the effect of logistics demand prediction,and be used to other prediction fields.

References:

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Last Update: 2018-02-28