Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality Article Swipe
Wentai Wu
,
Ligang He
,
Weiwei Lin
,
Yi Su
,
Yuhua Cui
,
Carsten Maple
,
Stephen A. Jarvis
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2020.3035685
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tkde.2020.3035685
On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency. \n
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tkde.2020.3035685
- OA Status
- green
- Cited By
- 62
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3095970110
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3095970110Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tkde.2020.3035685Digital Object Identifier
- Title
-
Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-SeasonalityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-03Full publication date if available
- Authors
-
Wentai Wu, Ligang He, Weiwei Lin, Yi Su, Yuhua Cui, Carsten Maple, Stephen A. JarvisList of authors in order
- Landing page
-
https://doi.org/10.1109/tkde.2020.3035685Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1908.01146Direct OA link when available
- Concepts
-
Computer science, Time series, Anomaly detection, Series (stratigraphy), Seasonality, Scheme (mathematics), Anomaly (physics), Data mining, Artificial intelligence, Machine learning, Mathematics, Biology, Condensed matter physics, Physics, Paleontology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
62Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 19, 2023: 12, 2022: 8, 2021: 8Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3095970110 |
|---|---|
| doi | https://doi.org/10.1109/tkde.2020.3035685 |
| ids.doi | https://doi.org/10.1109/tkde.2020.3035685 |
| ids.mag | 3095970110 |
| ids.openalex | https://openalex.org/W3095970110 |
| fwci | 4.11206736 |
| type | article |
| title | Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality |
| awards[0].id | https://openalex.org/G5706756861 |
| awards[0].funder_id | https://openalex.org/F4320334627 |
| awards[0].display_name | |
| awards[0].funder_award_id | EP/N02298X/1 |
| awards[0].funder_display_name | Engineering and Physical Sciences Research Council |
| awards[1].id | https://openalex.org/G6863711020 |
| awards[1].funder_id | https://openalex.org/F4320335787 |
| awards[1].display_name | |
| awards[1].funder_award_id | 2019ZD26 |
| awards[1].funder_display_name | Fundamental Research Funds for the Central Universities |
| awards[2].id | https://openalex.org/G3407485131 |
| awards[2].funder_id | https://openalex.org/F4320324202 |
| awards[2].display_name | |
| awards[2].funder_award_id | 2017B010126002 |
| awards[2].funder_display_name | Guangdong Science and Technology Department |
| awards[3].id | https://openalex.org/G6720523971 |
| awards[3].funder_id | https://openalex.org/F4320334627 |
| awards[3].display_name | |
| awards[3].funder_award_id | EP/N02334X/1 |
| awards[3].funder_display_name | Engineering and Physical Sciences Research Council |
| awards[4].id | https://openalex.org/G3918016905 |
| awards[4].funder_id | https://openalex.org/F4320321001 |
| awards[4].display_name | |
| awards[4].funder_award_id | 61872084 |
| awards[4].funder_display_name | National Natural Science Foundation of China |
| awards[5].id | https://openalex.org/G1159932308 |
| awards[5].funder_id | https://openalex.org/F4320334898 |
| awards[5].display_name | |
| awards[5].funder_award_id | 201902010040 |
| awards[5].funder_display_name | Guangzhou Science and Technology Program key projects |
| awards[6].id | https://openalex.org/G5091187257 |
| awards[6].funder_id | https://openalex.org/F4320334898 |
| awards[6].display_name | |
| awards[6].funder_award_id | 201807010052 |
| awards[6].funder_display_name | Guangzhou Science and Technology Program key projects |
| awards[7].id | https://openalex.org/G8569276088 |
| awards[7].funder_id | https://openalex.org/F4320321001 |
| awards[7].display_name | |
| awards[7].funder_award_id | 61772205 |
| awards[7].funder_display_name | National Natural Science Foundation of China |
| awards[8].id | https://openalex.org/G4734878561 |
| awards[8].funder_id | https://openalex.org/F4320334627 |
| awards[8].display_name | |
| awards[8].funder_award_id | EP/R007195/1 |
| awards[8].funder_display_name | Engineering and Physical Sciences Research Council |
| awards[9].id | https://openalex.org/G4138581256 |
| awards[9].funder_id | https://openalex.org/F4320334898 |
| awards[9].display_name | |
| awards[9].funder_award_id | 201802010010 |
| awards[9].funder_display_name | Guangzhou Science and Technology Program key projects |
| awards[10].id | https://openalex.org/G5160284993 |
| awards[10].funder_id | https://openalex.org/F4320334898 |
| awards[10].display_name | |
| awards[10].funder_award_id | 201907010001 |
| awards[10].funder_display_name | Guangzhou Science and Technology Program key projects |
| biblio.issue | 9 |
| biblio.volume | 34 |
| biblio.last_page | 4160 |
| biblio.first_page | 4147 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Anomaly Detection Techniques and Applications |
| topics[1].id | https://openalex.org/T12205 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9997000098228455 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Time Series Analysis and Forecasting |
| topics[2].id | https://openalex.org/T10400 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9922999739646912 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Network Security and Intrusion Detection |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320324202 |
| funders[1].ror | https://ror.org/00tjzgn92 |
| funders[1].display_name | Guangdong Science and Technology Department |
| funders[2].id | https://openalex.org/F4320334627 |
| funders[2].ror | https://ror.org/0439y7842 |
| funders[2].display_name | Engineering and Physical Sciences Research Council |
| funders[3].id | https://openalex.org/F4320334898 |
| funders[3].ror | https://ror.org/00tjzgn92 |
| funders[3].display_name | Guangzhou Science and Technology Program key projects |
| funders[4].id | https://openalex.org/F4320335787 |
| funders[4].ror | |
| funders[4].display_name | Fundamental Research Funds for the Central Universities |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7016996741294861 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C151406439 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6717092990875244 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[1].display_name | Time series |
| concepts[2].id | https://openalex.org/C739882 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6517467498779297 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[2].display_name | Anomaly detection |
| concepts[3].id | https://openalex.org/C143724316 |
| concepts[3].level | 2 |
| concepts[3].score | 0.609696626663208 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[3].display_name | Series (stratigraphy) |
| concepts[4].id | https://openalex.org/C125403950 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5875373482704163 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2111082 |
| concepts[4].display_name | Seasonality |
| concepts[5].id | https://openalex.org/C77618280 |
| concepts[5].level | 2 |
| concepts[5].score | 0.535467803478241 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1155772 |
| concepts[5].display_name | Scheme (mathematics) |
| concepts[6].id | https://openalex.org/C12997251 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4480854570865631 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[6].display_name | Anomaly (physics) |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4371642470359802 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3526342511177063 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.28441858291625977 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.12309637665748596 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C86803240 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[11].display_name | Biology |
| concepts[12].id | https://openalex.org/C26873012 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[12].display_name | Condensed matter physics |
| concepts[13].id | https://openalex.org/C121332964 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[13].display_name | Physics |
| concepts[14].id | https://openalex.org/C151730666 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[14].display_name | Paleontology |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7016996741294861 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/time-series |
| keywords[1].score | 0.6717092990875244 |
| keywords[1].display_name | Time series |
| keywords[2].id | https://openalex.org/keywords/anomaly-detection |
| keywords[2].score | 0.6517467498779297 |
| keywords[2].display_name | Anomaly detection |
| keywords[3].id | https://openalex.org/keywords/series |
| keywords[3].score | 0.609696626663208 |
| keywords[3].display_name | Series (stratigraphy) |
| keywords[4].id | https://openalex.org/keywords/seasonality |
| keywords[4].score | 0.5875373482704163 |
| keywords[4].display_name | Seasonality |
| keywords[5].id | https://openalex.org/keywords/scheme |
| keywords[5].score | 0.535467803478241 |
| keywords[5].display_name | Scheme (mathematics) |
| keywords[6].id | https://openalex.org/keywords/anomaly |
| keywords[6].score | 0.4480854570865631 |
| keywords[6].display_name | Anomaly (physics) |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.4371642470359802 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.3526342511177063 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.28441858291625977 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.12309637665748596 |
| keywords[10].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1109/tkde.2020.3035685 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S30698027 |
| locations[0].source.issn | 1041-4347, 1558-2191, 2326-3865 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1041-4347 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE Transactions on Knowledge and Data Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310320439 |
| locations[0].source.host_organization_name | IEEE Computer Society |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320439, https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | IEEE Computer Society, Institute of Electrical and Electronics Engineers |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Transactions on Knowledge and Data Engineering |
| locations[0].landing_page_url | https://doi.org/10.1109/tkde.2020.3035685 |
| locations[1].id | pmh:oai:wrap.warwick.ac.uk:144208 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306400665 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Warwick Research Archive Portal (University of Warwick) |
| locations[1].source.host_organization | https://openalex.org/I39555362 |
| locations[1].source.host_organization_name | University of Warwick |
| locations[1].source.host_organization_lineage | https://openalex.org/I39555362 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | acceptedVersion |
| locations[1].raw_type | Journal Article |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | |
| locations[2].id | pmh:oai:arXiv.org:1908.01146 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | https://arxiv.org/pdf/1908.01146 |
| locations[2].version | submittedVersion |
| locations[2].raw_type | text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | http://arxiv.org/abs/1908.01146 |
| locations[3].id | pmh:oai:pure.atira.dk:openaire_cris_publications/37bc9fd9-5289-42db-90a4-68c26ab58bcc |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400216 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | Research Portal (King's College London) |
| locations[3].source.host_organization | https://openalex.org/I183935753 |
| locations[3].source.host_organization_name | King's College London |
| locations[3].source.host_organization_lineage | https://openalex.org/I183935753 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | article |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Wu, W, He, L, Lin, W, Su, Y, Cui, Y, Maple, C & Jarvis, S 2022, 'Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series with Multi-Seasonality', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 9, pp. 4147-4160. https://doi.org/10.1109/TKDE.2020.3035685 |
| locations[3].landing_page_url | https://research.birmingham.ac.uk/en/publications/37bc9fd9-5289-42db-90a4-68c26ab58bcc |
| indexed_in | arxiv, crossref |
| authorships[0].author.id | https://openalex.org/A5030234311 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5851-327X |
| authorships[0].author.display_name | Wentai Wu |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I39555362 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[0].institutions[0].id | https://openalex.org/I39555362 |
| authorships[0].institutions[0].ror | https://ror.org/01a77tt86 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I39555362 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University of Warwick |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wentai Wu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[1].author.id | https://openalex.org/A5064596191 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5671-0576 |
| authorships[1].author.display_name | Ligang He |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I39555362 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[1].institutions[0].id | https://openalex.org/I39555362 |
| authorships[1].institutions[0].ror | https://ror.org/01a77tt86 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I39555362 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | University of Warwick |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ligang He |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[2].author.id | https://openalex.org/A5007440245 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6876-1795 |
| authorships[2].author.display_name | Weiwei Lin |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I90610280 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, South China University of Technology, Guangzhou, China |
| authorships[2].institutions[0].id | https://openalex.org/I90610280 |
| authorships[2].institutions[0].ror | https://ror.org/0530pts50 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I90610280 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | South China University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Weiwei Lin |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Computer Science and Engineering, South China University of Technology, Guangzhou, China |
| authorships[3].author.id | https://openalex.org/A5110394340 |
| authorships[3].author.orcid | https://orcid.org/0009-0002-5580-0075 |
| authorships[3].author.display_name | Yi Su |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I39555362 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[3].institutions[0].id | https://openalex.org/I39555362 |
| authorships[3].institutions[0].ror | https://ror.org/01a77tt86 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I39555362 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of Warwick |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yi Su |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Computer Science, University of Warwick, Coventry, U.K. |
| authorships[4].author.id | https://openalex.org/A5037165576 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Yuhua Cui |
| authorships[4].affiliations[0].raw_affiliation_string | Research Institute of Worldwide Byte Information Security, Jinan, China |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yuhua Cui |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Research Institute of Worldwide Byte Information Security, Jinan, China |
| authorships[5].author.id | https://openalex.org/A5080175512 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-4715-212X |
| authorships[5].author.display_name | Carsten Maple |
| authorships[5].countries | GB |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I39555362 |
| authorships[5].affiliations[0].raw_affiliation_string | Warwick Manufacturer Group, University of Warwick, Coventry, U.K. |
| authorships[5].institutions[0].id | https://openalex.org/I39555362 |
| authorships[5].institutions[0].ror | https://ror.org/01a77tt86 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I39555362 |
| authorships[5].institutions[0].country_code | GB |
| authorships[5].institutions[0].display_name | University of Warwick |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Carsten Maple |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Warwick Manufacturer Group, University of Warwick, Coventry, U.K. |
| authorships[6].author.id | https://openalex.org/A5051267613 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1249-2167 |
| authorships[6].author.display_name | Stephen A. Jarvis |
| authorships[6].countries | GB |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I79619799 |
| authorships[6].affiliations[0].raw_affiliation_string | College of Engineering and Physical Sciences, University of Birmingham, Birmingham, U.K. |
| authorships[6].institutions[0].id | https://openalex.org/I79619799 |
| authorships[6].institutions[0].ror | https://ror.org/03angcq70 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I79619799 |
| authorships[6].institutions[0].country_code | GB |
| authorships[6].institutions[0].display_name | University of Birmingham |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Stephen Jarvis |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | College of Engineering and Physical Sciences, University of Birmingham, Birmingham, U.K. |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/1908.01146 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2020-11-09T00:00:00 |
| display_name | Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Anomaly Detection Techniques and Applications |
| related_works | https://openalex.org/W2806741695, https://openalex.org/W4290647774, https://openalex.org/W3189286258, https://openalex.org/W3207797160, https://openalex.org/W3210364259, https://openalex.org/W4300558037, https://openalex.org/W2667207928, https://openalex.org/W2912112202, https://openalex.org/W4377864969, https://openalex.org/W3120251014 |
| cited_by_count | 62 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 15 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 19 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 12 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 8 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 8 |
| locations_count | 4 |
| best_oa_location.id | pmh:oai:arXiv.org:1908.01146 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/1908.01146 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/1908.01146 |
| primary_location.id | doi:10.1109/tkde.2020.3035685 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S30698027 |
| primary_location.source.issn | 1041-4347, 1558-2191, 2326-3865 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1041-4347 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Transactions on Knowledge and Data Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310320439 |
| primary_location.source.host_organization_name | IEEE Computer Society |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320439, https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | IEEE Computer Society, Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Transactions on Knowledge and Data Engineering |
| primary_location.landing_page_url | https://doi.org/10.1109/tkde.2020.3035685 |
| publication_date | 2020-11-03 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2795891046, https://openalex.org/W2318460400, https://openalex.org/W2071377705, https://openalex.org/W2105497548, https://openalex.org/W6723306929, https://openalex.org/W2044777538, https://openalex.org/W2437244913, https://openalex.org/W1995443851, https://openalex.org/W2123256336, https://openalex.org/W2614786548, https://openalex.org/W2113271473, https://openalex.org/W2029438113, https://openalex.org/W2078559879, https://openalex.org/W2702877955, https://openalex.org/W6718456096, https://openalex.org/W2410999552, https://openalex.org/W2810872865, https://openalex.org/W6635741669, https://openalex.org/W849628863, https://openalex.org/W2278984902, https://openalex.org/W2676890506, https://openalex.org/W2186910770, https://openalex.org/W6720514713, https://openalex.org/W2620661538, https://openalex.org/W2922207751, https://openalex.org/W2897710418, https://openalex.org/W1992866626, https://openalex.org/W2747599906, https://openalex.org/W6631190155, https://openalex.org/W6675354045, https://openalex.org/W6628678161, https://openalex.org/W6732899423, https://openalex.org/W6756753118, https://openalex.org/W2361765050, https://openalex.org/W2093564481, https://openalex.org/W2109316012, https://openalex.org/W2137130182, https://openalex.org/W2404692435, https://openalex.org/W2461729787, https://openalex.org/W2963541464, https://openalex.org/W119403003, https://openalex.org/W2022771005, https://openalex.org/W2026430219, https://openalex.org/W2278186031, https://openalex.org/W2085866051, https://openalex.org/W2056999868, https://openalex.org/W2929084559, https://openalex.org/W2950729596, https://openalex.org/W2895891814, https://openalex.org/W2622612680, https://openalex.org/W2608190038, https://openalex.org/W2101234009, https://openalex.org/W1442292319, https://openalex.org/W2490586035, https://openalex.org/W2902455138, https://openalex.org/W1598358187, https://openalex.org/W4293718132, https://openalex.org/W2474046725, https://openalex.org/W4297814361, https://openalex.org/W1522301498, https://openalex.org/W3105668694, https://openalex.org/W2437679399, https://openalex.org/W2964121744, https://openalex.org/W1815076433, https://openalex.org/W4250863759, https://openalex.org/W4237069999 |
| referenced_works_count | 66 |
| abstract_inverted_index.a | 8, 65, 93, 101, 117, 133 |
| abstract_inverted_index.In | 86 |
| abstract_inverted_index.We | 150 |
| abstract_inverted_index.an | 125 |
| abstract_inverted_index.as | 17 |
| abstract_inverted_index.be | 82 |
| abstract_inverted_index.in | 4, 12, 51, 61, 132, 142, 182 |
| abstract_inverted_index.is | 7, 57 |
| abstract_inverted_index.of | 2, 32, 38, 68, 88, 110, 144, 147, 184 |
| abstract_inverted_index.to | 81, 155 |
| abstract_inverted_index.we | 90, 115 |
| abstract_inverted_index.LTI | 131 |
| abstract_inverted_index.The | 169 |
| abstract_inverted_index.and | 24, 35, 84, 107, 124, 136, 166 |
| abstract_inverted_index.its | 145 |
| abstract_inverted_index.key | 9 |
| abstract_inverted_index.our | 157, 174 |
| abstract_inverted_index.the | 29, 36, 48, 105, 108, 185, 195 |
| abstract_inverted_index.Area | 189 |
| abstract_inverted_index.both | 163 |
| abstract_inverted_index.data | 25, 33, 53, 71, 139 |
| abstract_inverted_index.each | 138 |
| abstract_inverted_index.fast | 83 |
| abstract_inverted_index.from | 162 |
| abstract_inverted_index.have | 72, 91, 151 |
| abstract_inverted_index.less | 59 |
| abstract_inverted_index.make | 40 |
| abstract_inverted_index.many | 62 |
| abstract_inverted_index.more | 43 |
| abstract_inverted_index.show | 172 |
| abstract_inverted_index.such | 16 |
| abstract_inverted_index.task | 42 |
| abstract_inverted_index.than | 45 |
| abstract_inverted_index.that | 129, 173 |
| abstract_inverted_index.this | 41 |
| abstract_inverted_index.time | 5, 69, 111 |
| abstract_inverted_index.used | 11, 187 |
| abstract_inverted_index.with | 159 |
| abstract_inverted_index.Curve | 191 |
| abstract_inverted_index.Local | 120 |
| abstract_inverted_index.Trend | 121 |
| abstract_inverted_index.Under | 190 |
| abstract_inverted_index.being | 148 |
| abstract_inverted_index.data. | 113 |
| abstract_inverted_index.ever. | 46 |
| abstract_inverted_index.large | 66 |
| abstract_inverted_index.light | 87 |
| abstract_inverted_index.means | 54 |
| abstract_inverted_index.model | 103 |
| abstract_inverted_index.needs | 80 |
| abstract_inverted_index.novel | 118 |
| abstract_inverted_index.point | 140 |
| abstract_inverted_index.rapid | 49 |
| abstract_inverted_index.smart | 21 |
| abstract_inverted_index.terms | 143, 183 |
| abstract_inverted_index.this, | 89 |
| abstract_inverted_index.which | 99 |
| abstract_inverted_index.while | 193 |
| abstract_inverted_index.(AUC), | 192 |
| abstract_inverted_index.(LTI), | 123 |
| abstract_inverted_index.adopts | 100 |
| abstract_inverted_index.cases. | 63 |
| abstract_inverted_index.center | 26 |
| abstract_inverted_index.manner | 135 |
| abstract_inverted_index.public | 164 |
| abstract_inverted_index.scheme | 175 |
| abstract_inverted_index.scores | 137 |
| abstract_inverted_index.sensor | 22 |
| abstract_inverted_index.series | 6, 70, 112 |
| abstract_inverted_index.system | 19 |
| abstract_inverted_index.On-line | 0 |
| abstract_inverted_index.anomaly | 78, 96, 179 |
| abstract_inverted_index.complex | 73 |
| abstract_inverted_index.demands | 39 |
| abstract_inverted_index.desired | 196 |
| abstract_inverted_index.metric, | 119, 188 |
| abstract_inverted_index.on-line | 77 |
| abstract_inverted_index.portion | 67 |
| abstract_inverted_index.propose | 116 |
| abstract_inverted_index.results | 171 |
| abstract_inverted_index.robotic | 18 |
| abstract_inverted_index.scheme, | 98 |
| abstract_inverted_index.several | 160 |
| abstract_inverted_index.sources | 34 |
| abstract_inverted_index.variety | 37 |
| abstract_inverted_index.various | 13 |
| abstract_inverted_index.Firstly, | 47 |
| abstract_inverted_index.Further, | 114 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.Thirdly, | 76 |
| abstract_inverted_index.backbone | 102 |
| abstract_inverted_index.becoming | 58 |
| abstract_inverted_index.commonly | 186 |
| abstract_inverted_index.computes | 130 |
| abstract_inverted_index.datasets | 161 |
| abstract_inverted_index.evaluate | 156 |
| abstract_inverted_index.existing | 177 |
| abstract_inverted_index.increase | 50 |
| abstract_inverted_index.learning | 56 |
| abstract_inverted_index.networks | 23 |
| abstract_inverted_index.robustly | 141 |
| abstract_inverted_index.suitable | 60 |
| abstract_inverted_index.Secondly, | 64 |
| abstract_inverted_index.achieving | 194 |
| abstract_inverted_index.algorithm | 128, 158 |
| abstract_inverted_index.anomalies | 3 |
| abstract_inverted_index.combining | 104 |
| abstract_inverted_index.conducted | 152 |
| abstract_inverted_index.detection | 1, 79, 97, 127, 180 |
| abstract_inverted_index.developed | 92 |
| abstract_inverted_index.diversity | 31 |
| abstract_inverted_index.efficient | 126 |
| abstract_inverted_index.extensive | 153 |
| abstract_inverted_index.features. | 75 |
| abstract_inverted_index.inference | 109 |
| abstract_inverted_index.real-time | 134 |
| abstract_inverted_index.reliable. | 85 |
| abstract_inverted_index.scenarios | 15 |
| abstract_inverted_index.security. | 27 |
| abstract_inverted_index.technique | 10 |
| abstract_inverted_index.unlabeled | 52 |
| abstract_inverted_index.algorithms | 181 |
| abstract_inverted_index.anomalous. | 149 |
| abstract_inverted_index.increasing | 30 |
| abstract_inverted_index.production | 167 |
| abstract_inverted_index.supervised | 55 |
| abstract_inverted_index.challenging | 44 |
| abstract_inverted_index.monitoring, | 20 |
| abstract_inverted_index.outperforms | 176 |
| abstract_inverted_index.probability | 146 |
| abstract_inverted_index.seasonality | 74 |
| abstract_inverted_index.experimental | 170 |
| abstract_inverted_index.repositories | 165 |
| abstract_inverted_index.unsupervised | 95 |
| abstract_inverted_index.Inconsistency | 122 |
| abstract_inverted_index.decomposition | 106 |
| abstract_inverted_index.environments. | 168 |
| abstract_inverted_index.representative | 178 |
| abstract_inverted_index.event-sensitive | 14 |
| abstract_inverted_index.experimentation | 154 |
| abstract_inverted_index.efficiency. \n | 197 |
| abstract_inverted_index.prediction-driven, | 94 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.94625799 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |