计算机应用与软件2025，Vol.42Issue(5)：56-61,6.DOI:10.3969/j.issn.1000-386x.2025.05.009
基于矩阵分解的不同缺失模式下库存缺失数据插补模型研究
A MATRIX DECOMPOSITION-BASED MODEL FOR INTERPOLATION OF MISSING INVENTORY DATA UNDER DIFFERENT MISSING PATTERNS
邹昕彤 1金辉1
作者信息
- 1. 辽宁工业大学汽车与交通工程学院 辽宁锦州 121000
- 折叠
摘要
Abstract
A missing inventory data interpolation model based on improved matrix decomposition is designed for inventory missing data.According to the characteristics of inventory data,the unit root test and Nemenyi post-hoc multiple comparison was adopted to analyze the data stationarity and significance.For missing data with different missing patterns,a time regularizer long short-term memory neural network was introduced to obtain the time dependence in time series data,and a space regularizer graph Laplacian was used to consider the spatiotemporal characteristics by taking advantage of the spatial correlation among network sensors.Meanwhile,an Adam optimizer was added to achieve high-performance interpolation of inventory missing data.According to the data characteristics,the RMSE evaluation metric was adopted for model evaluation.Through comparative studies with advanced methods,it is proved that the model has superior interpolation performance.关键词
数字物流/库存管理/缺失数据插补/时间序列/矩阵分解/Adam优化器Key words
Digital logistics/Inventory management/Missing data interpolation/Time series/Matrix decomposition/Adam optimizer分类
信息技术与安全科学引用本文复制引用
邹昕彤,金辉..基于矩阵分解的不同缺失模式下库存缺失数据插补模型研究[J].计算机应用与软件,2025,42(5):56-61,6.
