A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series
Author:
Affiliation:
1. School of Software, Southeast University,Nanjing,China
2. College of Computer Science and Engineering, Southeast University,Nanjing,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10548022/10548040/10548882.pdf?arnumber=10548882
Reference12 articles.
1. Deep Learning-Based PM2.5 Long Time-Series Prediction by Fusing Multisource Data—A Case Study of Beijing
2. Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review
3. Correlation-Aware Spatial–Temporal Graph Learning for Multivariate Time-Series Anomaly Detection
4. Symbolic Aggregate Approximation (SAX)
5. Extended SAX: Extension of symbolic aggregate approximation for financial time series data representation[J];Lkhagva;DEWS2006,2006
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