Name
Abstract | ||
|---|---|---|
Outlier detection schemes have been used to identify the unwanted noise and this helps us to obtain underlying valuable signals and predicting the next state of the systems/signals. However, there are few researches on sequential outlier detection in time series although a lot of outlier detection algorithms arc developed in off-line systems. In this paper, we focus on the sequential (on-line) outlier detection schemes, that are based on the 'delete-replace' approach. We also demonstrate that three different types of residuals can be used to design the outlier detection scheme to achieve accurate sequential estimation: marginal residual, conditional residual, and contribution. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Outlier detection,Marginal residual,Conditional residual,Contribution |
Field | DocType | ISSN |
Anomaly detection,Signal processing,Residual,Time series,Pattern recognition,Computer science,Artificial intelligence,Sequential estimation,Trajectory | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Won Yoon | 1 | 112 | 23.94 |