实验科学与技术2025，Vol.23Issue(6)：34-42,9.DOI:10.12179/1672-4550.20250301
具有缺失值及异常值的时间序列处理与再筛选机制
Processing and Reselection Mechanism for Time Series with Missing Values and Outliers
李逸君 1王思淼 1赵沐歌 1吴然超2
作者信息
- 1. 安徽大学纽约石溪学院,合肥 230039
- 2. 安徽大学纽约石溪学院,合肥 230039||安徽大学数学科学学院,合肥 230039
- 折叠
摘要
Abstract
Multidimensional time series data are widely used,but can be rendered unreliable due to missing values or outliers.A multidimensional time series data processing and reselection mechanism(MTSM)method is proposed in this paper.This method is based on Transformer-based imputation for missing values,combined with the 3σ rule and box plots for outlier detection and hierarchical correction.Multi-scale fuzzy entropy,boundary mixture resampling and Gaussian mixture clustering sampling are applied according to data types to re-screen the imputed and corrected data.A comparative analysis was conducted based on the COVID-19 data from the World Health Organization,and the results show that the MTSM method outperforms GRU,RNN,and LATC at different missing and outlier rates,and also demonstrates outstanding accuracy and robustness.关键词
时间序列/缺失值填补/异常值处理/再筛选机制/MTSM方法Key words
time series/missing value imputation/outlier handling/reselection mechanism/MTSM method分类
信息技术与安全科学引用本文复制引用
李逸君,王思淼,赵沐歌,吴然超..具有缺失值及异常值的时间序列处理与再筛选机制[J].实验科学与技术,2025,23(6):34-42,9.
