Name
Title | ||
|---|---|---|
SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining. |
Abstract | ||
|---|---|---|
•SAX-ARM algorithm is proposed for discovering rules from multivariate time series.•Inverse normal transformation (INT) is adopted for normalizing time series.•Symbolic aggregate approximation (SAX) is applied to discretize time series.•Association rule mining (ARM) discovers frequent patterns among deviant events.•A die-casting manufacturing dataset is used to illustrate the propose method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2019.112950 | Expert Systems with Applications |
Keywords | Field | DocType |
Multivariate time series,Event pattern discovery,Inverse normal transformation (INT),Symbolic aggregate approximation (SAX),Association rule mining (ARM) | Data mining,Normal distribution,Symbolic aggregate approximation,Computer science,Multivariate statistics,Outlier,System monitoring,Association rule learning,Artificial intelligence,Normal-inverse Gaussian distribution,Manufacturing process,Machine learning | Journal |
Volume | ISSN | Citations |
141 | 0957-4174 | 2 |
PageRank | References | Authors |
0.36 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoonseok Park | 1 | 2 | 0.36 |
| Jae-Yoon Jung | 2 | 297 | 31.94 |