Replies to This Discussion

Permalink Reply by Carlos Lizárraga Celaya on April 28, 2016 at 2:12pm

Hi, 

I have found this Blog with several tools and examples about Anomaly study cases:

https://anomaly.io/blog/

Hope it helps

Carlos

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Permalink Reply by bk ramesh on May 2, 2016 at 1:26am

Hello

I came across IntelliPredikt who have developed Time Series based Anomaly detection using HTM algorithm for streaming Starter Motor sensor data.  If you are interested to learn more please reach them on [email protected]

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Permalink Reply by Sunil M on May 2, 2016 at 2:47am

             Thanks Ramesh for the suggestion..:)

bk ramesh said:

Hello

I came across IntelliPredikt who have developed Time Series based Anomaly detection using HTM algorithm for streaming Starter Motor sensor data.  If you are interested to learn more please reach them on [email protected]

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Permalink Reply by Sunil M on May 2, 2016 at 2:47am

     Thanks Carlos for your suggestion...:)

Carlos Lizárraga Celaya said:

Hi, 

I have found this Blog with several tools and examples about Anomaly study cases:

https://anomaly.io/blog/

Hope it helps

Carlos

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Permalink Reply by Sione Palu on May 23, 2016 at 10:44am

Sunil,  I've used Kolmogorov-Smirnov test (KS for short) before for outlier detection which is good.  Break up your time-series data into a sliding windows with an option of overlap.  The input is now a matrix of mxn, ie,  m  rows and  n columns.  m is the number of windows and n is the number of time-period per window.  The KS will tell you which window/s  is the outlier, it may be multiple windows or just a single or even no outlier found. What language you develop in? I mainly work in Matlab & Java.  There are tons of others for use in outlier/anomaly detection for time-series, so if you work in matlab or java then I can show you more & where to get those packages from each authors' sites.

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Permalink Reply by Gustavo Pereira on September 6, 2016 at 11:12am

Look at the Netflix Surus/RAD package.

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Permalink Reply by Ranjit Mishra on September 7, 2016 at 4:20am

There are various packages for anomaly detection, but you can also start with simple things such as -

• How the data looks. Use scatter plot to see the shape of data.
• Look at variance, i.e. volatility in business lingo.
• Creating a TS model .. may be use SMA to start with. Then move on to check the followings - CFE, MAD, MSE and MAPE -  these parameters give a good insight into data behaviors.

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Permalink Reply by Rama Govardhana on October 25, 2017 at 1:01am

I am spreading this in my network, will come back when I get some info for you. Cheers.

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Permalink Reply by Majid ALDOSARI on September 15, 2018 at 10:00am

i wrote a whole thesis on it using LSTMs http://mars.gmu.edu/handle/1920/10250 . . you may not want to do that so i have a literature review in it too to guide you.

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Permalink Reply by Robert R. Tucci on June 4, 2020 at 11:18am

https://qbnets.wordpress.com/fraud-detection-bayesian-networks/

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