I'm tring to use an algorithm for finding and ranking continuing decreases in my data. The base condition:
The breakpoint location (blue arrow) is unknown but bigger than x (can be on t= 4 or in t=9 but can't be smaller than t=2 for example )
Part B (after the breakpoint) has smaller AVG than Part A (before the breakpoint) significantly given the data variance.
The larger the ratio between part A and part B, the higher the score.
Need suggestions for the best practice.
UPDATE: I spit the X axis to last 2 days and 10 days before those days. and used SUM((x_last_2_days -x_avg last_10_days)/std_over_all )
So I got the following patterns
Rank 2 - fine
Rank 3 - controversial
Rank 6 - bad
Rank 7 - good
Rank 8 - bad
How to ignore the bad pattern or improve the algo.
It looks like you are dealing with a noisy time series. Your problem as formulated is known step detection or edge detection. Your approach to solve it is listed in the wiki there (look for sliding window). Because the data is noisy, you do have to employ statistical methods to detect features in the noisy data to differentiate a change in the pattern from pure noise. Concrete implementation would require some analysis and possibly simulations.
In general, analysis of time series and signal processing involves de-trending, de-seasoning, edge detection, so that all that remains is pure white noise. Tools used are Fourier transform, autocorrelation, etc.