My External Post on Capitalone.com/tech/ - Optimizing Your Public Cloud for Maximum Efficiency
Below are my comments to the following post of my former manager:
What do I miss? (working 100% at home)
Definitely ! I used to do that a lot:
- explaining my ideas to developers, so they could implement what want not blindly but with passion. Now working from home I spent twice more time via zoom and still not sure I ignite that passion;
- proving my new and innovative concepts to my boss. Now they have to listen or reading my Ruglish, respectively my ability to convince accepting my ideas is declining...
Less impactful hallway or over-the-cube-wall conversations can also solve problems and create bonds.....
I feel that too. But in DevOps-Adgile-Slack-zoom environment I am getting used to get what I need. My hobby to be always on-line blogging-presenting (Now it is Virtual Convergence/Seminar) helps a lot.
How to find a change in the historical time-series data?
Long ago I have developed a method to do that which is based on EV data (Exception Value - a magnitude of anomalies collected historically). Idea: any change occurred first would appear as anomaly and then become a normality (norm), so collecting and analyzing a severity of all anomalies opens possibility to find phases in the history with different patterns. To detect that mathematically one needs just finds all roots of the following equation: EV(t)=0 , where t is time. But it is too simple as that might give you too many change points. To control the the sensitivity of detecting change points the method should have some sensitivity tuning parameters,such as following:
N - normality confidence band in percentiles = UCL-LCL (if it is 100%, that means all observations is normal, 0% means all observations abnormal)
Where UCL is upper control limit and LCL is lower control limit. Why "||" (absolute value)? To catch two types of changes: going up- and downwards.