How can I simply calculate the rolling/moving variance of a time series in python?

Question

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)

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.

Answer 1

You should take a look at pandas. For example:

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')