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I'd like to develop an anomaly detection. I have historical data from sensors in the form of time series. The time series can be divided into data of a normal state and data of an abnormal state i.e. I have good data and bad data from sensors. Before I want to implement the actual anomaly detection, I want to investigate whether the good data and bad data can be separated by an explorative analysis. Thereby I want to find out if I can distinguish between good data and bad data. I hope to find out if anomalies can be detected later. Now I am looking for statistical methods or algorithmic procedures to detect and visualize this.

Can anyone help me or has ideas how I can analyse this?

Thanks!

makome
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  • The bible on this thread starts here http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html and continues here http://faculty.chicagobooth.edu/ruey.tsay/teaching/uts/lec10-08.pdf and here https://stats.stackexchange.com/questions/169468/how-to-do-forecasting-with-detection-of-outliers-in-r-time-series-analysis-pr – IrishStat Apr 09 '19 at 21:11
  • @Irish I'm having a hard time seeing the applicability of your methods to the present question: it concerns a situation where there appears to be a potential to characterize the anomalous patterns, perhaps quite precisely. One needn't search for just any "intervention," but can focus--to great advantage--on detecting the targeted patterns. Makome, it would help to clarify your question concerning the nature of these anomalies and your objectives. For instance, would this be *post hoc* analysis or would you need to detect a new occurrence of an anomaly as quickly as possible in real time? – whuber Apr 09 '19 at 22:08
  • what i inferred ( perhaps mistakingly) was that normal observations were being observed and then at subsequent chronological periods potentially unusual observations were recorded. The mission was to red-flag them as soon as they occurred. – IrishStat Apr 09 '19 at 23:07

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