Timeline for Early anomaly detection / Failure prediction on time series
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 22, 2023 at 10:12 | vote | accept | Andrea | ||
| Feb 13, 2023 at 23:39 | comment | added | Jon Nordby | With anomaly detection, you would only train on normal / non-failing data You would use the labeled failure for validation/test sets, to check whether the anomaly scores correspond to failures or not. There are many approaches and models possible. | |
| Feb 13, 2023 at 23:36 | comment | added | Jon Nordby | Have you established that it is indeed possible to predict the failure? If you do a backwards analysis on the events you have, can you find leading indicators of the failure event? Cause if a skilled data analyst cannot find such patterns, then it weakens the hypothesis that it exists in the data - which is a necessary precondition to solving it in an automated way | |
| Feb 13, 2023 at 23:33 | history | edited | Jon Nordby | CC BY-SA 4.0 |
clarify anomaly detection
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| Feb 13, 2023 at 22:24 | comment | added | Andrea | Now the model based on domain knowledge has to be powered because it does not give decent results. I don't think the time-to-failure prediction approach could work because it's not a specific component of the platform that fails, but the entire structure that is way complex. I would like to go with unsupervised learning, but I am not sure how to make the network map the input with a failure ahead of time. Should I feed it past time series in which the platform did not have problems? And how to check when the network starts behaving in an anomalous way? | |
| Feb 12, 2023 at 21:36 | history | answered | Jon Nordby | CC BY-SA 4.0 |