@inproceedings{2a891b0cfbed46f9a0d774b722ac5ce1,
title = "Design of a Highly Robust Regression Rule Mining Algorithm for Time Series Data",
abstract = "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.",
keywords = "approximate rule mining, missing value imputation, regression model",
author = "Yiming Guan and Donghua Yang and Mengmeng Li and Hongzhi Wang and Hongqiang Wang and Sijia Zheng and Xiaoqian Meng and Siyan Zhu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 19th International Conference on Green, Pervasive, and Cloud Computing, GPC 2024 ; Conference date: 27-09-2024 Through 30-09-2024",
year = "2026",
doi = "10.1007/978-981-95-1346-8\_9",
language = "英语",
isbn = "9789819513451",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "133--148",
editor = "Xiaobo Zhou and Chen Yu and Song Guo and Jianping Wang and Xianhua Song and Zeguang Lu",
booktitle = "Green, Pervasive, and Cloud Computing - 19th International Conference, GPC 2024, Proceedings",
address = "德国",
}