Scholarlink

Time series data, widely used in industrial and scientific domains, often contain noise such as outliers and missing values, which degrade rule mining performance. To address this, we propose a robust regression rule mining algorithm tailored for time series data. We introduce Approximate Conditional Regression Rules (ACRR), which relax strict rule constraints to uncover approximate attribute relationships. The algorithm combines predicate-based condition generation with linear regression validation, and incorporates modules for outlier detection and iterative missing value imputation. Experiments on real-world datasets demonstrate that our method effectively discovers meaningful rules and maintains robustness in noisy environments.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design of a Highly Robust Regression Rule Mining Algorithm for Time Series Data

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Yiming Guan,
  • Donghua Yang,
  • Mengmeng Li,
  • Hongzhi Wang,
  • Hongqiang Wang,
  • Sijia Zheng,
  • Xiaoqian Meng,
  • Siyan Zhu

摘要

Time series data, widely used in industrial and scientific domains, often contain noise such as outliers and missing values, which degrade rule mining performance. To address this, we propose a robust regression rule mining algorithm tailored for time series data. We introduce Approximate Conditional Regression Rules (ACRR), which relax strict rule constraints to uncover approximate attribute relationships. The algorithm combines predicate-based condition generation with linear regression validation, and incorporates modules for outlier detection and iterative missing value imputation. Experiments on real-world datasets demonstrate that our method effectively discovers meaningful rules and maintains robustness in noisy environments.