Fan Lilin, Liu Xia, Mao Wentao, Yang Kai, Song Zhaoyu
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
Entropy (Basel). 2023 May 7;25(5):764. doi: 10.3390/e25050764.
The demand for complex equipment aftermarket parts is mostly sporadic, showing typical intermittent characteristics as a whole, resulting in the evolution law of a single demand series having insufficient information, which restricts the prediction effect of existing methods. To solve this problem, this paper proposes a prediction method of intermittent feature adaptation from the perspective of transfer learning. Firstly, to extract the intermittent features of the demand series, an intermittent time series domain partitioning algorithm is proposed by mining the demand occurrence time and demand interval information in the series, then constructing the metrics, and using a hierarchical clustering algorithm to divide all the series into different sub-source domains. Secondly, the intermittent and temporal characteristics of the sequence are combined to construct a weight vector, and the learning of common information between domains is accomplished by weighting the distance of the output features of each cycle between domains. Finally, experiments are conducted on the actual after-sales datasets of two complex equipment manufacturing enterprises. Compared with various prediction methods, the method in this paper can effectively predict future demand trends, and the prediction's stability and accuracy are significantly improved.
复杂设备售后市场零部件的需求大多是零星的，整体呈现典型的间歇性特征，导致单一需求序列的演化规律信息不足，这限制了现有方法的预测效果。为解决这一问题，本文从迁移学习的角度提出一种间歇性特征自适应预测方法。首先，为提取需求序列的间歇性特征，通过挖掘序列中的需求发生时间和需求间隔信息，提出一种间歇性时间序列域划分算法，然后构建度量指标，并使用层次聚类算法将所有序列划分为不同的子源域。其次，结合序列的间歇性和时间性特征构建权重向量，通过对各周期域间输出特征的距离进行加权来完成域间公共信息的学习。最后，在两家复杂设备制造企业的实际售后数据集上进行实验。与各种预测方法相比，本文方法能够有效预测未来需求趋势，预测的稳定性和准确性显著提高。
