Timeline for (Multivariate) anomaly detection of (redundant) sensor data
Current License: CC BY-SA 4.0
3 events
| when toggle format | what | by | license | comment | |
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| Jul 9 at 13:40 | comment | added | noNameTed | It depends on the model and library you choose. For something that is intended for a time series such as an RNN each input should be a multivariate time series. Computational burden increases with the length of the series. How long you make each series and the amount you shift the starting point between subsequent series is really up to you. For something like item 4 you estimate a mean and covariance of your sensor data (ignoring time/autocorrelation) from your training data and apply a chi-squared test to data you are evaluating. | |
| Jun 16 at 17:42 | comment | added | Alexander | Thanks a lot for the response! I will take a look at the approaches tomorrow. However, a question already now: What would you feed those methods? Assume I have 10 sensors facing to 2 different directions (5 each), so for every day, my data has the shape 96x10. Would you consider it sensor wise (10 data points of length 96) or timestamp-wise (96 data points of length 10), or something different? | |
| Jun 13 at 12:53 | history | answered | noNameTed | CC BY-SA 4.0 |