Been really bewildered by this situation - I'm running a correlation between 'A' and 'B'; and 'C' and 'D'. I've encountered 2 scenarios which I can't really explain:
1) High correlation between absolute values of 'A' and 'B' but low correlation between 'change in A' and 'change in B'.
2) Negative correlation between absolute values of 'C' and 'D', but relatively strong positive correlation between 'change in C' and 'change in D'.
AFAIK - Scenario (1) implies that the deviations from the mean absolute value move in the same direction, but deviations from the mean changes don't move together as well. But what does this truly mean? How can I interpret this in a lay-man's sense?
No real idea about scenario (2)...
Edit: Some details to make the question clearer:
A and B are both represent time-series data (monthly) for 36 periods (3 years).
A = 100, 120, 140,...., 280, 300.
B = 10, 20, 30,...., 90, 110.
The correlation between the absolute values is relatively high. But, it may not always be accurate to correlate absolute values, so I want to understand whether % change in A and % change in B are correlated (I remember while working with stocks, we were always told to use returns rather than prices).
Change in A (only 35 data points) = 20%, 16.67%,..., 7.1%
Change in B (only 35 data points) = 100%, 50%,..., 22%.
Satterplot to visualise - https://imgur.com/a/W0apDm1