让我们假设我们有一个数据集，它大概是
import numpy as np
x = np.linspace(0,2*np.pi,100)
y = np.sin(x) + np.random.random(100) * 0.2
Therefore we have a variation of 20% of the dataset. My first idea was to use the UnivariateSpline function of scipy, but the problem is that this does not consider the small noise in a good way. If you consider the frequencies, the background is much smaller than the signal, so a spline only of the cutoff might be an idea, but that would involve a back and forth fourier transformation, which might result in bad behaviour.
Another way would be a moving average, but this would also need the right choice of the delay.
有什么提示/书籍或链接可以解决这个问题吗?
编辑:看看这个答案。使用np。Cumsum比np.卷积快得多
我使用了一种快速而肮脏的方法来平滑数据，基于移动平均盒(通过卷积):
x = np.linspace(0,2*np.pi,100)
y = np.sin(x) + np.random.random(100) * 0.8
def smooth(y, box_pts):
box = np.ones(box_pts)/box_pts
y_smooth = np.convolve(y, box, mode='same')
return y_smooth
plot(x, y,'o')
plot(x, smooth(y,3), 'r-', lw=2)
plot(x, smooth(y,19), 'g-', lw=2)
对于我的一个项目，我需要为时间序列建模创建间隔，为了使过程更高效，我创建了tsmoothie:一个python库，用于以向量化的方式平滑时间序列和异常值检测。
它提供了不同的平滑算法以及计算间隔的可能性。
这里我使用了一个convolutionsmooth，但是你也可以测试其他的。
import numpy as np
import matplotlib.pyplot as plt
from tsmoothie.smoother import *
x = np.linspace(0,2*np.pi,100)
y = np.sin(x) + np.random.random(100) * 0.2
# operate smoothing
smoother = ConvolutionSmoother(window_len=5, window_type='ones')
smoother.smooth(y)
# generate intervals
low, up = smoother.get_intervals('sigma_interval', n_sigma=2)
# plot the smoothed timeseries with intervals
plt.figure(figsize=(11,6))
plt.plot(smoother.smooth_data[0], linewidth=3, color='blue')
plt.plot(smoother.data[0], '.k')
plt.fill_between(range(len(smoother.data[0])), low[0], up[0], alpha=0.3)
我还指出tsmoothie可以用向量化的方式对多个时间序列进行平滑
