如何在 python 中简单地计算时间序列的滚动/移动方差?

2022-01-11 00:00:00 python numpy time-series variance sliding-window

问题描述

我有一个简单的时间序列，我正在努力估计移动窗口内的方差.更具体地说，我无法弄清楚与实现滑动窗口功能的方式有关的一些问题.例如，当使用 NumPy 且窗口大小 = 20 时:

I have a simple time series and I am struggling to estimate the variance within a moving window. More specifically, I cannot figure some issues out relating to the way of implementing a sliding window function. For example, when using NumPy and window size = 20:

def rolling_window(a, window):
    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
    strides = a.strides + (a.strides[-1],)
    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) 

rolling_window(data, 20)
np.var(rolling_window(data, 20), -1)
datavar=np.var(rolling_window(data, 20), -1)

也许我在某个地方弄错了，在这个思路上.有谁知道一个简单的方法来做到这一点?任何帮助/建议都将受到欢迎.

Perhaps I am mistaken somewhere, in this line of thought. Does anyone know a straightforward way to do this? Any help/advice would be most welcome.

解决方案

你应该看看 pandas.例如:

import pandas as pd
import numpy as np

# some sample data
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000)).cumsum()

#plot the time series
ts.plot(style='k--')

# calculate a 60 day rolling mean and plot
pd.rolling_mean(ts, 60).plot(style='k')

# add the 20 day rolling variance:
pd.rolling_std(ts, 20).plot(style='b')

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