Release Time:2022-07-14  Hits:

Date of Publication: 2017-01-01

Journal: 信息与控制

Institution: 经济管理学院

Volume: 46

Issue: 4

Page Number: 495-502

ISSN: 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.

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