面向装备制造业的非平稳时间序列需求组合预测方法  

Combination Forecasting Method of Non-stationary Time Series Demand on the Equipment Manufacturing Industry

作　　者：白朝阳[1] 胡子涵[1] 刘晓莹[1] 

机构地区：[1]大连理工大学管理与经济学部,辽宁大连116024

出　　处：《信息与控制》2017年第4期495-502,共8页Information and Control

基　　金：国家科技支撑计划资助项目(2015BAF08B02);教育部人文社会科学研究项目(14YJCZH001);辽宁省社科规划基金项目(L16BGL010)

摘　　要：针对装备制造业的物料需求量时间序列数据非平稳的特性,提出一种基于经验模态分解(EMD)和最小二乘支持向量机(LSSVM)的组合预测模型.运用EMD方法将非平稳时间序列分解为一系列的本征模函数(IMF)和一个残差项(Res),然后结合业务实际将各IMF合成为高频、低频两部分,分别代表短期波动和长期趋势,挖掘出更多的信息再结合最小二乘支持向量回归(LSSVR)模型进行组合预测.实证结果表明EMD-LSSVR组合预测可以高效预测非平稳物料需求时间序列且相比传统方法预测精度较高.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 EMDLSSVR can forecast the non-stationary time series of material demand efficiently,and its prediction accuracy is higher than that of traditional methods.

关 键 词：经验模态分解(EMD) 最小二乘支持向量回归(LSSVR) 组合预测 物料需求 装备制造业 

分 类 号：O211.61[理学—概率论与数理统计] TP18[理学—数学]