Anomaly Detection SystemAnomaly detection in multiple parametersWhen to use what - Machine Learninganomaly detection alert systemAnomaly detection for transaction dataNetwork Anomaly detectionNetflow anomaly detection python packagesIntrusion Detection System (IDS)Anomaly detection with time seriesComparison between approaches for timeseries anomaly detectionAnomaly detection without any knowledge about structure
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Anomaly Detection System
Anomaly detection in multiple parametersWhen to use what - Machine Learninganomaly detection alert systemAnomaly detection for transaction dataNetwork Anomaly detectionNetflow anomaly detection python packagesIntrusion Detection System (IDS)Anomaly detection with time seriesComparison between approaches for timeseries anomaly detectionAnomaly detection without any knowledge about structure
$begingroup$
I need a sanity check. I want to create an anomaly detection system.
The logic which I am planning to use is the following:
- Find anomalies in the past using Seasonal Hybrid Extreme Studentized Deviate Test.
- Binarise the anomalies (1 the anomalies and 0 the trends).
- Run several algorithms (Autoencoders, SVM, Logistic Regression, Naive Bayes, Lasso Regression, etc) with variables that are correlated and validate the models and use it.
Does the binarisation process makes sense?
machine-learning machine-learning-model anomaly-detection binary anomaly
edited 3 hours ago
Stephen Rauch♦
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I need a sanity check. I want to create an anomaly detection system.
The logic which I am planning to use is the following:
- Find anomalies in the past using Seasonal Hybrid Extreme Studentized Deviate Test.
- Binarise the anomalies (1 the anomalies and 0 the trends).
- Run several algorithms (Autoencoders, SVM, Logistic Regression, Naive Bayes, Lasso Regression, etc) with variables that are correlated and validate the models and use it.
Does the binarisation process makes sense?
machine-learning machine-learning-model anomaly-detection binary anomaly
edited 3 hours ago
Stephen Rauch♦
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I need a sanity check. I want to create an anomaly detection system.
The logic which I am planning to use is the following:
- Find anomalies in the past using Seasonal Hybrid Extreme Studentized Deviate Test.
- Binarise the anomalies (1 the anomalies and 0 the trends).
- Run several algorithms (Autoencoders, SVM, Logistic Regression, Naive Bayes, Lasso Regression, etc) with variables that are correlated and validate the models and use it.
Does the binarisation process makes sense?
machine-learning machine-learning-model anomaly-detection binary anomaly
edited 3 hours ago
Stephen Rauch♦
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I need a sanity check. I want to create an anomaly detection system.
The logic which I am planning to use is the following:
- Find anomalies in the past using Seasonal Hybrid Extreme Studentized Deviate Test.
- Binarise the anomalies (1 the anomalies and 0 the trends).
- Run several algorithms (Autoencoders, SVM, Logistic Regression, Naive Bayes, Lasso Regression, etc) with variables that are correlated and validate the models and use it.
Does the binarisation process makes sense?
machine-learning machine-learning-model anomaly-detection binary anomaly
machine-learning machine-learning-model anomaly-detection binary anomaly
edited 3 hours ago
Stephen Rauch♦
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 3 hours ago
Stephen Rauch♦
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 3 hours ago
Stephen Rauch♦
1,52551330
edited 3 hours ago
Stephen Rauch♦
1,52551330
edited 3 hours ago
Stephen Rauch♦
1,52551330
1,52551330
asked 3 hours ago
AngelosAngelos
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 hours ago
AngelosAngelos
31
asked 3 hours ago
AngelosAngelos
31
31
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Angelos is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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$begingroup$
Yes, your logic and what you are thinking is excellent.
There is only a flaw in your thinking: The variables you run the model with must not necesarily be "correlated" in a linear sense of the word, just don't discard any variable because any of them could explain your binary output, and not have a linear relationship with it.
Is a common solution to binarise an output to detect anomalies, but you will lose the ability to predict "how much" outlier is an outlier, make sure you don't need this information after.
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
$endgroup$
add a comment |
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1 Answer
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1 Answer
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$begingroup$
Yes, your logic and what you are thinking is excellent.
There is only a flaw in your thinking: The variables you run the model with must not necesarily be "correlated" in a linear sense of the word, just don't discard any variable because any of them could explain your binary output, and not have a linear relationship with it.
Is a common solution to binarise an output to detect anomalies, but you will lose the ability to predict "how much" outlier is an outlier, make sure you don't need this information after.
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
$endgroup$
add a comment |
$begingroup$
Yes, your logic and what you are thinking is excellent.
There is only a flaw in your thinking: The variables you run the model with must not necesarily be "correlated" in a linear sense of the word, just don't discard any variable because any of them could explain your binary output, and not have a linear relationship with it.
Is a common solution to binarise an output to detect anomalies, but you will lose the ability to predict "how much" outlier is an outlier, make sure you don't need this information after.
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
$endgroup$
add a comment |
$begingroup$
Yes, your logic and what you are thinking is excellent.
There is only a flaw in your thinking: The variables you run the model with must not necesarily be "correlated" in a linear sense of the word, just don't discard any variable because any of them could explain your binary output, and not have a linear relationship with it.
Is a common solution to binarise an output to detect anomalies, but you will lose the ability to predict "how much" outlier is an outlier, make sure you don't need this information after.
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
$endgroup$
Yes, your logic and what you are thinking is excellent.
There is only a flaw in your thinking: The variables you run the model with must not necesarily be "correlated" in a linear sense of the word, just don't discard any variable because any of them could explain your binary output, and not have a linear relationship with it.
Is a common solution to binarise an output to detect anomalies, but you will lose the ability to predict "how much" outlier is an outlier, make sure you don't need this information after.
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
answered 3 hours ago
Juan Esteban de la CalleJuan Esteban de la Calle
72022
72022
add a comment |
add a comment |
Angelos is a new contributor. Be nice, and check out our Code of Conduct.
Angelos is a new contributor. Be nice, and check out our Code of Conduct.
Angelos is a new contributor. Be nice, and check out our Code of Conduct.
Angelos is a new contributor. Be nice, and check out our Code of Conduct.
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