A dynamic object may have following several phases in its lifetime:
1. Initial phase to set a norm - anomalies cannot be detected as there is no baseline sample is established yet. Could be tired later as an outlier.
2. Stable period without any anomalies.
3. Unstable period when anomalies are appearing: suddenly or with gradually increasing rate.
4. Anomalies are introducing a new norm and the rate of anomalies is gradually decreasing.
5. =>2. The next stable period.
6. =>3. … and so on.
To detect those dynamic object phases one can use Anomaly and Change Point detection methods. One of them is SETDS (described in this blog), which has been implementing now as a www.Perfomalist.com tool.
Here is an example how the Perfomalist (Download Input Data Sample) test data is used to detect stable and unstable periods.
Data consists of 28 weeks. To see some dynamic and to catch when anomalies started appearing, the data was divided into 23 data sets.
- The 1st one has 4 initial weeks (initial baseline or reference/learning set) plus following week (1st "current" week).
- The 2nd one has 5 initial weeks as the next (on one week bigger) baseline and following week as the next "current" week.
- The 3rd one... the same mechanism as described above.
Then the www.Perfomalist.com was applied 23 times (could be automated using Personalist APIs) and results were combined into the spreadsheet.
The table and daily summarized charts are below. The result shows clearly 2nd (stable) and 3rd (unstable) phases.
