...性需求；不常用备件；少量历史数据；需求预测；指数平滑法 [gap=854]Keywords: intermittent demand; rarely used spare parts; a little of history data; request forecasting; single exponential ..
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According to this, the thesis does some research on support vector machines(SVMs)based forecasting method for rarely used spare parts demand.
基于此,本文研究了基于支持向量机的不常用备件需求预测方法。
参考来源 - 基于支持向量机的不常用备件需求预测方法研究To address this problem above, the thesis makes efforts on application of multiple support vector machines (SVM) to forecast rarely used spare parts demand via designing an integrated forecasting support system.
本文引入基于有限样本统计学习理论的支持向量机回归方法,预测不常用备件需求。
参考来源 - 集成多SVM的不常用备件需求预测支持系统研究·2,447,543篇论文数据，部分数据来源于NoteExpress
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