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')

Answer 2

The Pandas rolling_mean and rolling_std functions have been deprecated and replaced by a more general "rolling" framework. @elyase's example can be modified to:

import pandas as pd
import numpy as np
%matplotlib inline

# 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
ts.rolling(window=60).mean().plot(style='k')

# add the 20 day rolling standard deviation:
ts.rolling(window=20).std().plot(style='b')

The rolling function supports a number of different window types, as documented here. A number of functions can be called on the rolling object, including var and other interesting statistics (skew, kurt, quantile, etc.). I've stuck with std since the plot is on the same graph as the mean, which makes more sense unit-wise.