↓ Skip to main content

Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges

Overview of attention for article published in Information Fusion, March 2023
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 548)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
16 news outlets
twitter
2 X users
patent
2 patents

Citations

dimensions_citation
104 Dimensions

Readers on

mendeley
111 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Published in
Information Fusion, March 2023
DOI 10.1016/j.inffus.2022.10.008
Authors

Li, Jason J. Jung

Timeline

Login to access the full chart related to this output.

If you don’t have an account, click here to discover Explorer

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 111 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 8%
Researcher 7 6%
Student > Master 6 5%
Student > Doctoral Student 5 5%
Lecturer 4 4%
Other 17 15%
Unknown 63 57%
Readers by discipline Count As %
Computer Science 28 25%
Engineering 10 9%
Social Sciences 3 3%
Unspecified 2 2%
Energy 1 <1%
Other 3 3%
Unknown 64 58%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 122. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2024.
All research outputs
#341,983
of 25,392,582 outputs
Outputs from Information Fusion
#5
of 548 outputs
Outputs of similar age
#8,165
of 422,415 outputs
Outputs of similar age from Information Fusion
#1
of 23 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 548 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 422,415 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.