4 ms·
Really depends on the use case. Working in healthcare, vital signs can be modeled as time series points, but are lower frequency than, say, metrics from servers
by bruth 8y ago
Really depends on the use case. Working in healthcare, vital signs can be modeled as time series points, but are lower frequency than, say, metrics from servers. However we want to store every point so a spike is not missed. One could argue an unsustained spike is noise, but in the healthcare domain there may be a correlation with some external event (the purpose is surprised and their heart rate spikes).
- have_faith 8y agoThe clever thing to do in this scenario would be to keep every spike but delete all the data between similar data points after storing. So you get low granularity for identical/nearly-the-same data points and high granularity when something interesting happens. I don't have any experience with time-series data so maybe this is commonplace.
- lozenge 8y agoThat would be impossible to run any new analyses on. What some would do is record in blocks where every point after the earliest is stored as a delta. Then each block is more compressible as it contains a lot of 0s.
- deleted 8y ago[deleted]