utilization_curves: Utilization curve data
In pridiltal/outfit: Outlier Detection in Multivariate Functional Data
Description
Usage
Format
Details
Author(s)
Description
A multivariate dataset containing a collection of
utilization curves
Usage
1
Format
A data frame with 60835 rows and 5 variables:
- basisid
Basis ID
- id
ID should be interpreted as a pair (cpu, session),
where both cpu and session are thought of as coming from
functions and have clear nonlinear dependencies. Most ID are
dense with up to 200 instances, though some are sparse
- cpu
CPU
- y
y
- subspaceid
An identifier classifying the type of shape.
There are 4 types of typical shapes and 4 types of
anomalous shapes.
Details
As the dataset contains commercially sensitive information,
the actual data source is not given here.All labels of product
and time are removed for confidentiality.
Author(s)
Priyanga Dilini Talagala, Cole Sodja
pridiltal/outfit documentation built on Aug. 13, 2020, 12:32 a.m.
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Description Usage Format Details Author(s)
Description
A multivariate dataset containing a collection of utilization curves
Usage
1 |
Format
A data frame with 60835 rows and 5 variables:
- basisid
Basis ID
- id
ID should be interpreted as a pair (cpu, session), where both cpu and session are thought of as coming from functions and have clear nonlinear dependencies. Most ID are dense with up to 200 instances, though some are sparse
- cpu
CPU
- y
y
- subspaceid
An identifier classifying the type of shape. There are 4 types of typical shapes and 4 types of anomalous shapes.
Details
As the dataset contains commercially sensitive information, the actual data source is not given here.All labels of product and time are removed for confidentiality.
Author(s)
Priyanga Dilini Talagala, Cole Sodja
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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