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Temporal Outlier Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 23 |
| Year of Publication: 2021 |
| Authors: Sunil Kumar Rajwar, I. Mukherjee, Pankaj Kumar Manjhi |
10.5120/ijca2021921071
|
Sunil Kumar Rajwar, I. Mukherjee, Pankaj Kumar Manjhi . Temporal Outlier Analysis. International Journal of Computer Applications. 174, 23 ( Mar 2021), 26-29. DOI=10.5120/ijca2021921071
Abstract
The main focus of this research is temporal data. Temporal data means data depends on time. A large number of applications generate a set of temporal data. For example, in our daily life there are different types of records such as credit, personal, financial, judicial, medical, etc. All depends on time. This highlights the need for a detailed organized study of temporal outlier analysis. Over the past decade, a great deal of research has been done on various types of temporal data, including consecutive data snapshots, a series of data snapshots, and data streams. In addition to initial work on time series, the researchers focused on rich data types that include multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier analysis, the techniques for temporal outlier analysis are very different, such as AR models, Markov models, evolutionary clustering, etc.
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