In data analysis, moving averages are often used to "smooth" noisy data. In terms of signal processing, applying a moving average is a very crude low-pass filter. In fact, a moving average filter is a special case of an FIR (finite impulse response) filter.
Here is a study I wrote in Python using numpy and matplotlib.
import numpy as np
from matplotlib import pyplot as plt
# calculate moving average and add zero-padding
# the function takes two parameter, the data array and the
# number of bins for averaging, default 5 can be overwritten
def moving_average(a, n=5):
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return (np.insert((ret[n-1:]/n),0,[0]*(n-1)))
# create some sort of noisy data
data = np.arange(100)
data = np.sin(data/5)+np.cos(data/20)+np.random.randn(100)/5
data_avg = moving_average(data,7)
print(data)
print(data_avg)
plt.plot(data,'.')
plt.plot(data_avg,'.')
plt.show()
