郑州大学学报（工学版）2024，Vol.45Issue(4)：79-86,8.DOI:10.13705/j.issn.1671-6833.2024.01.007
基于张量表示的间歇性序列自适应区间预测
Adaptive Interval Prediction of Intermittent Series Based on Tensor Representation
摘要
Abstract
In the actual business,parts demand occured randomly and demand fluctuates,so the demand sequence for spare parts showed obvious intermittent distribution.At the same time,due to factors such as manual reporting errors or special events,the actual demand for spare parts was prone to abnormal changes,making it difficult for traditional time series prediction methods to capture the evolution of the demand for accessories,resulting in high uncertainty and insufficient reliability of prediction results.To solve this problem,an adaptive interval prediction method for intermittent series based on tensor representation was proposed.Firstly,hierarchical clustering was used to screen similar sequences based on the average demand interval and square of the coefficient of variation of acces-sory sequences,forming sequence clusters to increase predictability.Secondly,the original demand sequence was reconstructed by tensor decomposition.The outliers in the sequence were then corrected while retaining the core in-formation of the original sequence to maximum extent.Finally,an adaptive prediction interval algorithm was con-structed,which could obtain the predicted value and prediction interval of the parts demand through the dynamic update mechanism to ensure the reliability of the results.The proposed method was validated on the aftersales data-set from a large vehicle manufacturing enterprise.Compared with existing time series prediction methods,the pro-posed method could effectively extract the evolutionary trend of various types of intermittent series and improve the prediction accuracy on the intermittent time series with small size as well.Experiments showed that the average root mean square scaled error(RMSSE)of this method was 0.32 lower than that of the mainstream in-depth learning method of demand prediction.More importantly,when the prediction results were distorted,the proposed method could provide a reliable and flexible prediction interval,which could be helpful to provide a feasible solution for in-telligent parts management.关键词
需求预测/间歇性时间序列/张量分解/配件管理/区间预测/时间序列聚类Key words
demand forecast/intermittent time series/tensor decomposition/parts management/interval predic-tion/time series clustering分类
信息技术与安全科学引用本文复制引用
毛文涛,高祥,罗铁军,张艳娜,宋钊瑜..基于张量表示的间歇性序列自适应区间预测[J].郑州大学学报（工学版）,2024,45(4):79-86,8.基金项目
国家重点研发计划项目(2018YFB1701400) （2018YFB1701400）
盾构及掘进技术国家重点实验室开放课题(SKLST-2021-K04) （SKLST-2021-K04）
