I have generated fake data with a gauss function, the data are generated from ~8AM to ~6PM for 30 days with the related timestamp.
When the data starts or ends, I frequently get spikes that trigger the anomaly detection algorithm.
Observing the data in "visualize" that depending on the bucket size the algorithm (avg) actually show random spikes:
But zooming, the data are actually as expected.
To reproduce the bug, you can try to import data generated with my python script:
https://github.com/MCdeamon7/data-generator
import the data and then try to make an anomaly detection job on it
I imagine that is something about bucket size because by adjusting it from 5 to 10 or to 15 the spikes disappear in some places and reappear in other places.
I'm using it on a docker container on Linux recompiled by me from version 2.11.0 (i modified just the max features from 5 to 10)
Sorry for my English and thanks to everyone that can help me.
I have generated fake data with a gauss function, the data are generated from ~8AM to ~6PM for 30 days with the related timestamp. When the data starts or ends, I frequently get spikes that trigger the anomaly detection algorithm.
Observing the data in "visualize" that depending on the bucket size the algorithm (avg) actually show random spikes:
But zooming, the data are actually as expected.
To reproduce the bug, you can try to import data generated with my python script: https://github.com/MCdeamon7/data-generator import the data and then try to make an anomaly detection job on it
I imagine that is something about bucket size because by adjusting it from 5 to 10 or to 15 the spikes disappear in some places and reappear in other places.
I'm using it on a docker container on Linux recompiled by me from version 2.11.0 (i modified just the max features from 5 to 10)
Sorry for my English and thanks to everyone that can help me.