Proceedings of the 2018 International Conference on Education Reform and Management Science (ERMS 2018)

Material Demand Combination Forecasting Model Based on EMD-PSO-LSSVR

Authors
Hua Mo, Lin Xiong, Ruo-yu Lu
Corresponding Author
Hua Mo
Available Online April 2018.
DOI
10.2991/erms-18.2018.62How to use a DOI?
Keywords
EMD, IMF, LSSVR
Abstract

The time series data of material demand of manufacturing companies are often non-stationary. Paper uses empirical mode decomposition (EMD) to convert non-stationary time series into a series of intrinsic mode function (IMF) and a residual term (RES), and then digged out more information combined with least squares support vector machine regression (LSSVR) to forecast the model. Finally, the empirical results show that the EMD-LSSVR combination forecast can effectively predict non-stationary material demand time series, and the prediction accuracy is high. It has a certain degree of promotion and practical value.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Education Reform and Management Science (ERMS 2018)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
April 2018
ISBN
978-94-6252-524-5
ISSN
2352-5398
DOI
10.2991/erms-18.2018.62How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hua Mo
AU  - Lin Xiong
AU  - Ruo-yu Lu
PY  - 2018/04
DA  - 2018/04
TI  - Material Demand Combination Forecasting Model Based on EMD-PSO-LSSVR
BT  - Proceedings of the 2018 International Conference on Education Reform and Management Science (ERMS 2018)
PB  - Atlantis Press
SP  - 347
EP  - 356
SN  - 2352-5398
UR  - https://doi.org/10.2991/erms-18.2018.62
DO  - 10.2991/erms-18.2018.62
ID  - Mo2018/04
ER  -