NumPy's lack of a particular domain-specific function is perhaps due to the Core Team's discipline and fidelity to NumPy's prime directive: provide an N-dimensional array type, as well as functions for creating, and indexing those arrays. Like many foundational objectives, this one is not small, and NumPy does it brilliantly.
The (much) larger SciPy contains a much larger collection of domain-specific libraries (called subpackages by SciPy devs)--for instance, numerical optimization (optimize), signal processsing (signal), and integral calculus (integrate).
My guess is that the function you are after is in at least one of the SciPy subpackages (scipy.signal perhaps); however, i would look first in the collection of SciPy scikits, identify the relevant scikit(s) and look for the function of interest there.
Scikits are independently developed packages based on NumPy/SciPy and directed to a particular technical discipline (e.g., scikits-image, scikits-learn, etc.) Several of these were (in particular, the awesome OpenOpt for numerical optimization) were highly regarded, mature projects long before choosing to reside under the relatively new scikits rubric. The Scikits homepage liked to above lists about 30 such scikits, though at least several of those are no longer under active development.
Following this advice would lead you to scikits-timeseries; however, that package is no longer under active development; In effect, Pandas has become, AFAIK, the de facto NumPy-based time series library.
Pandas has several functions that can be used to calculate a moving average; the simplest of these is probably rolling_mean, which you use like so:
>>> # the recommended syntax to import pandas
>>> import pandas as PD
>>> import numpy as NP
>>> # prepare some fake data:
>>> # the date-time indices:
>>> t = PD.date_range('1/1/2010', '12/31/2012', freq='D')
>>> # the data:
>>> x = NP.arange(0, t.shape[0])
>>> # combine the data & index into a Pandas 'Series' object
>>> D = PD.Series(x, t)
Now, just call the function rolling_mean passing in the Series object and a window size, which in my example below is 10 days.
>>> d_mva = PD.rolling_mean(D, 10)
>>> # d_mva is the same size as the original Series
>>> d_mva.shape
(1096,)
>>> # though obviously the first w values are NaN where w is the window size
>>> d_mva[:3]
2010-01-01 NaN
2010-01-02 NaN
2010-01-03 NaN
verify that it worked--e.g., compared values 10 - 15 in the original series versus the new Series smoothed with rolling mean
>>> D[10:15]
2010-01-11 2.041076
2010-01-12 2.041076
2010-01-13 2.720585
2010-01-14 2.720585
2010-01-15 3.656987
Freq: D
>>> d_mva[10:20]
2010-01-11 3.131125
2010-01-12 3.035232
2010-01-13 2.923144
2010-01-14 2.811055
2010-01-15 2.785824
Freq: D
The function rolling_mean, along with about a dozen or so other function are informally grouped in the Pandas documentation under the rubric moving window functions; a second, related group of functions in Pandas is referred to as exponentially-weighted functions (e.g., ewma, which calculates exponentially moving weighted average). The fact that this second group is not included in the first (moving window functions) is perhaps because the exponentially-weighted transforms don't rely on a fixed-length window