Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network Article Swipe
Hailong Xie
,
Chenxian Hao
,
Jie Li
,
Min Li
,
Peng Luo
,
Jinpeng Zhu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijcce.2022.10.001
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijcce.2022.10.001
Anomaly detection of time series data is one of the most important problems in industrial applications. Most of methods are difficult to obtain rich features of samples due to the lack of dimension. In this paper, we develop an anomaly detection method for time series data based on multi-granularity neighbor residual network (MGNRN). First, we construct a neighbor input vector with a sliding time window for each data sample, and define neighbor-based input matrix by considering multi-granularity neighborhood features. Second, we compute the linear and non-linear neighbor features of multi-granularity time windows for the sample. Finally, by combining the linear neighborhood residual with nonlinear residual, we predict the abnormal probability of the sample. Experiments verify the multi-granularity neighbor residual network improves the accuracy of abnormal detection, and show good performance on precision and F1 metrics.
Related Topics
Concepts
Granularity
Residual
Anomaly detection
Computer science
Data mining
Series (stratigraphy)
Dimension (graph theory)
Anomaly (physics)
Time series
Sample (material)
Nearest neighbor search
Algorithm
Pattern recognition (psychology)
Artificial intelligence
Mathematics
Machine learning
Paleontology
Operating system
Condensed matter physics
Physics
Biology
Chromatography
Pure mathematics
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ijcce.2022.10.001
- OA Status
- gold
- Cited By
- 6
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304688109
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4304688109Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ijcce.2022.10.001Digital Object Identifier
- Title
-
Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-01Full publication date if available
- Authors
-
Hailong Xie, Chenxian Hao, Jie Li, Min Li, Peng Luo, Jinpeng ZhuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ijcce.2022.10.001Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ijcce.2022.10.001Direct OA link when available
- Concepts
-
Granularity, Residual, Anomaly detection, Computer science, Data mining, Series (stratigraphy), Dimension (graph theory), Anomaly (physics), Time series, Sample (material), Nearest neighbor search, Algorithm, Pattern recognition (psychology), Artificial intelligence, Mathematics, Machine learning, Paleontology, Operating system, Condensed matter physics, Physics, Biology, Chromatography, Pure mathematics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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