问题描述
我试图检测"一个系列中速度的突然下降，但我不知道如何捕捉它.详细信息和代码如下:
I am trying to "detect" a sudden drop in velocity in a series and I'm not sure how to capture it. The details and code are below:
这是我拥有的系列片段以及用于生成它的代码:
This is a snippet of the Series that I have along with the code to produce it:
velocity_df.velocity.car1
Index velocity
200 17.9941
201 17.9941
202 18.4031
203 18.4031
这是整个系列的图
我正在尝试检测从 220 到 230-40 的突然下降，并将其保存为如下所示的系列:
I'm trying to detect the sudden drop from 220 to 230-40 and save that out as a Series that looks like this:
Index velocity
220 14.927
221 14.927
222 14.927
223 14.927
224 14.518
225 14.518
226 16.1538
227 12.2687
228 9.20155
229 6.33885
230 4.49854
我只是想在速度突然下降时捕捉一个大概的范围，以便使用其他功能.
I'm just trying to capture an approximate range when there is a sudden decrease in speed so as to use other features.
如果我可以添加任何其他信息，请告诉我.谢谢！
If I can add any additional information, please let me know.Thank you!
推荐答案
如果你想一个一个比较两个值，这将是一个简单的方法:
This would be a simple approach, if you want to compare two values one by one:
鉴于您的问题中名为 s 的系列，您可以通过将其减去 1 来构造数据的绝对离散导数:
Given the series from your question, called s you can construct the absolute discrete derivative of your data by subtracting it with a shift of 1:
d = pd.Series(s.values[1:] - s.values[:-1], index=s.index[:-1]).abs()
如果您现在取该系列绝对差值的最大值 m，您可以将其乘以一个介于 0 和 1 之间的因子 a 作为阈值:
If you now take the maximum m of that series of absolute differences, you can multiply it with a factor a between 0 and 1 as a threshold:
a = .7
m = d.max()
print(d > m * a)
最后一行输出匹配的索引.
The last line outputs the indices of the matches.
以此为基础，您可以使用滑动窗口技术，例如 核密度估计或 Parzen 窗口 创建更流畅的结果:
Building up on this, you could use a sliding window technique such as kernel density estimation, or Parzen window to create more smooth results:
r = d.rolling(3, min_periods=1, win_type='parzen').sum()
n = r.max()
就像之前我们可以打印出匹配的元素
Like before we can print out the matching elements
print(r > n * a)
给出以下输出
Index
220 False
221 False
222 False
223 False
224 False
225 False
226 False
227 True
228 True
229 True
dtype: bool
这篇关于如何检测 Pandas 中时间序列图的突然变化的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！