Fan Lilin, Zhang Jiahu, Mao Wentao, Cao Fukang
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
Entropy (Basel). 2023 Jan 7;25(1):123. doi: 10.3390/e25010123.
In the actual maintenance of manufacturing enterprises, abnormal changes in after-sale parts demand data often make the inventory strategies unreasonable. Due to the intermittent and small-scale characteristics of demand sequences, it is difficult to accurately identify the anomalies in such sequences using current anomaly detection algorithms. To solve this problem, this paper proposes an unsupervised anomaly detection method for intermittent time series. First, a new abnormal fluctuation similarity matrix is built by calculating the squared coefficient of variation and the maximum information coefficient from the macroscopic granularity. The abnormal fluctuation sequence can then be adaptively screened by using agglomerative hierarchical clustering. Second, the demand change feature and interval feature of the abnormal sequence are constructed and fed into the support vector data description model to perform hypersphere training. Then, the unsupervised abnormal point location detection is realized at the micro-granularity level from the abnormal sequence. Comparative experiments are carried out on the actual demand data of after-sale parts of two large manufacturing enterprises. The results show that, compared with the current representative anomaly detection methods, the proposed approach can effectively identify the abnormal fluctuation position in the intermittent sequence of small samples, and also obtain better detection results.
在制造企业的实际维护中，售后零部件需求数据的异常变化常常使库存策略变得不合理。由于需求序列具有间歇性和小规模的特点，利用当前的异常检测算法难以准确识别此类序列中的异常情况。为了解决这个问题，本文提出了一种针对间歇性时间序列的无监督异常检测方法。首先，从宏观粒度通过计算变异系数平方和最大信息系数构建一个新的异常波动相似性矩阵。然后，利用凝聚层次聚类对异常波动序列进行自适应筛选。其次，构建异常序列的需求变化特征和区间特征，并将其输入到支持向量数据描述模型中进行超球体训练。接着，从异常序列在微观粒度层面实现无监督异常点定位检测。对两家大型制造企业售后零部件的实际需求数据进行了对比实验。结果表明，与当前具有代表性的异常检测方法相比，所提方法能够有效识别小样本间歇性序列中的异常波动位置，并且还能获得更好的检测结果。
