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

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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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  -