间歇性时间序列的可预测性评估及联合预测方法

Predictability evaluation and joint forecasting method for intermittent time series

在高端制造企业的运维业务中,配件需求随机发生,且伴随有大量的零需求阶段,同时,对应的配件需求数据量小,且呈现出间歇性和块状分布的特点,导致现有时间序列预测方法难以有效预测配件需求走势。为解决该问题,提出了一种间歇性时间序列的可预测性评估及联合预测方法。首先,提出了一种新的间歇相似度指标,通过统计两条序列中“0”元素出现的频次和位置,并结合最大信息系数和平均需求间隔等度量指标,有效评估了序列的趋势信息和波动规律,并实现了对间歇性序列可预测性的量化;其次,基于该指标,构建了一个间歇相似度层次聚类方法来自适应地筛选相似性高、可预测性强的序列,剔除极度稀疏、无法预测的序列;此外,探索利用序列间的结构化信息,并构建多输出支持向量回归(M-SVR)模型,从而实现小样本下的间歇性序列联合预测;最后,分别在两个公开数据集(UCI礼品零售数据集和华为电脑配件数据集)和某大型制造企业实际配件售后数据集上进行实验。实验结果表明,相比多个典型的时间序列预测方法,所提方法可有效挖掘各类间歇性序列的可预测性,提高小样本间歇性序列的预测精度,从而为制造企业配件需求预测提供了一种新的解决方案。

In the operation and maintenance of high-end manufacturing enterprises,the spare parts demand occurs randomly,accompanied by a large number of zero demand periods. At the same time,the corresponding sparse parts demand data is of small scale and has intermittent and distribution with lump formation characteristics. Consequently,most of current time series forecasting methods are hard to effectively predict the demand trends. To solve this problem,a predictability evaluation and joint forecasting method for intermittent time series was proposed. Firstly,a new intermittentsimilarity metric was proposed. In this metric,the frequency and positions of the "0" element occurring in the two sequences were counted,while the metrics such as maximal information coefficient and average demand interval were combined to evaluate the tendency information and fluctuation pattern of the sequences effectively and realize the quantification of the predictability of the intermittent time series. Then,based on this metric,an intermittent-similarity hierarchical clustering method was constructed to adaptively select the sequences with high similarity and strong predictability as well as eliminate extremely sparse and unpredictable sequences. Moreover,the structured information between the sequences was explored and utilized,a Multi-output Support Vector Regression(M-SVR) model was constructed,thereby achieving the joint prediction of intermittent time series with small-scale data. Finally,the experiments were conducted on two public datasets(UCI(University of California Irvine)gift retail dataset and Huawei computer accessory dataset)and a real-world spare parts after-sales dataset of a large manufacturing enterprise,respectively. The results show that compared with several representative time series forecasting methods,the proposed method can effectively exploit the predictability of various kinds of intermittent sequences and improve the prediction accuracy of intermittent time series with small-scale data. Therefore,the proposed method provides a new solution for the spare parts demand forecasting of manufacturing enterprises.