Timeline for Design a function that indicates significant deviations in response times
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 10, 2021 at 9:06 | answer | added | pveentjer | timeline score: 1 | |
| May 23, 2021 at 18:16 | vote | accept | BMBM | ||
| May 23, 2021 at 11:26 | comment | added | BMBM | @amon, fair enough, in W1, t=12/13 should NOT have been marked. For W2 I think that would be OK to color t=38 maybe in orange or so, as a kind of "in between". What I'm struggling with a bit is for example the "drop off" part, so that I can gloss over drops of 1 or 2 mins but still return back to normal state as soon as the situation steadily normalizes (values are below threshold.) | |
| May 23, 2021 at 7:23 | answer | added | Bart van Ingen Schenau | timeline score: 3 | |
| May 22, 2021 at 22:25 | comment | added | amon | You have already described an approach that is close to an implementable algorithm, but it doesn't seem to match your graphics. For example, you've marked t=12 despite being under T1, and not marked T=38 despite being over T1. I wouldn't worry about normal distributions since a lot of statistics can still be done in a non-parametric manner. Percentiles are a robust metric that can be used without assuming any distribution. | |
| May 22, 2021 at 18:24 | comment | added | candied_orange | I’m not sure how you’ll define the difference between “normal” and “extreme” without assuming some kind of distribution. Don’t you need an assumed distribution to define what constitutes an outlier? | |
| May 22, 2021 at 15:20 | history | asked | BMBM | CC BY-SA 4.0 |