AnomalyDetection: Anomaly Detection Using Seasonal Hybrid Extreme Studentized...
In Harminder858/anomaly_twitter_harminder: Anomaly Detection Using Seasonal Hybrid Extreme Studentized Deviate Test
Description
A technique for detecting anomalies in seasonal univariate time series.
The methods uses are robust, from a statistical standpoint, in the presence of
seasonality and an underlying trend. These methods can be used in
wide variety of contexts. For example, detecting anomalies in system metrics after
a new software release, user engagement post an 'A/B' test, or for problems in
econometrics, financial engineering, political and social sciences.
Author(s)
Owen S. Vallis, Jordan Hochenbaum, Arun Kejariwal; Modernization
contributions by Bob Rudis
Harminder858/anomaly_twitter_harminder documentation built on March 29, 2020, 5:33 a.m.
R Package Documentation
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Description
A technique for detecting anomalies in seasonal univariate time series. The methods uses are robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. These methods can be used in wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an 'A/B' test, or for problems in econometrics, financial engineering, political and social sciences.
Author(s)
Owen S. Vallis, Jordan Hochenbaum, Arun Kejariwal; Modernization contributions by Bob Rudis
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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