问题描述
我希望使用熊猫rolling_window函数(窗口类型不是boxcar-最好是hamming)平滑"定期采样的30秒时间序列数据.但是，到目前为止，我尝试应用的所有窗口(从2到100的不同窗口长度)似乎都将平滑后的数据偏移为较低的值，例如:
I am looking to 'smooth' regularly-sampled 30-sec time series data using the pandas rolling_window function, with a window type other than boxcar - ideally hamming. However, so far all windows which I have tried to apply, over varying window lengths from 2 to 100, appear to offset the smoothed data to lower values, e.g.:
原始数据(浅蓝色)和数据通过pd.rolling_window(data,2,'hamming')运行.
Plot of raw data (light blue) and data run through pd.rolling_window(data,2,'hamming').
很明显，偏移量随时间变化.
Clearly, the quantity of offset varies through time.
这个结果不是我期望的-而是，我希望过滤后的数据集位于原始数据集的顶部.
This result isn't what I would expect - rather, I'd expect the filtered dataset to sit atop the raw one.
任何关于这里可能发生的情况的解释都是很好的.
Any explanations of what might be happening here would be great.
推荐答案
尝试对滚动窗口使用center=True参数.
Try the center=True argument to rolling_window.
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