Outlier detection for temporal data
by Gupta, Manish, (Computer scientist)
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, tim…
Read More ↓Title Statements
- Full title :Outlier detection for temporal data / Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han.
Summary
Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book.
Read More ↓Edition Statements
- Publication Date:©2014
Publication Statements
- Publisher(s):Springer
- Place of Publication:Cham, Switzerland
- Language:eng
- Publication Date:©2014
The Physical Object
- Description:1 online resource (xviii, 110 pages) : : illustrations (some color)
Edition Identifiers
- ISBN:9781627053761, 162705376X, 9783031019050, 3031019059
- DOI:10.1007/978-3-031-01905-0
- oclc:ocn877885434
- LC Classification:QA276
- Dewey Decimal:519.5
Table of Contents
Introduction and challenges -- Outlier detection for time series and data sequences -- Outlier detection for data streams -- Outlier detection for distributed data streams -- Outlier detection for spatio-temporal data -- Outlier detection for temporal network data --Applications of outlier detection for temporal data -- Conclusions and research directions.
1. Introduction and challenges -- 1.1 Temporal outlier examples -- 1.2 Different facets of temporal outlier analysis -- 1.3 Specific challenges for outlier detection for temporal data -- 1.4 Conclusions and summary.
2. Outlier detection for time series and data sequences -- 2.1 Outliers in time series databases -- 2.1.1 Direct detection of outlier time series -- 2.1.2 Window-based detection of outlier time series -- 2.1.3 Outlier subsequences in a test time series -- 2.1.4 Outlier points across multiple time series -- 2.2 Outliers within a given time series -- 2.2.1 Points as outliers -- 2.2.2 Subsequences as outliers -- 2.3 Conclusions and summary.
3. Outlier detection for data streams -- 3.1 Evolving prediction models -- 3.1.1 Online sequential discounting -- 3.1.2 Dynamic cluster maintenance -- 3.1.3 Dynamic Bayesian netwo…
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