Hits:

Date of Publication:2017-01-01

Journal:信息与控制

Affiliation of Author(s):经济管理学院

Volume:46

Issue:4

Page Number:495-502

ISSN No.:1002-0411

Abstract:In accordance with the characteristics of a non-stationary material requirement time series of the equipment manufacturing industry, we build a combination forecasting model based on empirical mode decomposition (EMD) and least square support vector regression (LSSVR).We divide the non-stationary time series into a series of intrinsic mode functions (IMF) and a residual by using EMD.We then analyze the business in a real situation and combine every IMF into high frequency and low frequency, which represent short-term fluctuations and long-term trends, respectively.After these steps, we mine more information.Then, we make a combination forecast by using LSSVR.An empirical study shows that the combination forecast of the EMD-LSSVR can forecast the non-stationary time series of material demand efficiently, and its prediction accuracy is higher than that of traditional methods.

Note:新增回溯数据