转换数据上持续减少模式的最佳算法
我正在使用算法来查找和排名我的数据持续减少。 基本条件：
断点位置（蓝色箭头）未知，但大于x（可以在t = 4或t = 9中，但不能小于t = 2）
B部分B（断点之后）具有考虑到数据差异，高于A部分（在断点之前）小的AVG。
A部分和B部分之间的比率越大，分数越高。
需要最佳实践的建议。
更新：我将X轴吐到持续2天的时间前10天。 并用过的sum（（x_last_2_days -x_avg last_10_days）/std_over_all）
，所以我得到以下模式
排名2-罚款
排名3-有争议的
排名6-不好
排名7-好
排名8-不好
如何忽略不良模式或改善算法。
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.
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看来您正在处理嘈杂的时间序列。您的问题已知步骤检测或边缘检测。您的解决方法在其中的Wiki中列出（寻找滑动窗口）。由于数据是嘈杂的，因此您必须采用统计方法来检测嘈杂数据中的特征，以区分模式的变化与纯噪声。具体的实施将需要一些分析甚至可能进行仿真。
通常，对时间序列和信号处理的分析涉及去趋势，去稳定，边缘检测，因此所有残留的都是纯白噪声。使用的工具为傅立叶变换，自相关，等等。
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.