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.