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Streaming statistics

Updated (d64c4a5)

Situations where streaming statistics are useful:

  • Unknown number of observations
  • Online streaming data
  • Dataset too big for local processing

For the remainder, let’s consider a set of observations $y_i$, weights $w_i$, such that

$$ y_1,\dots,y_i \in \mathbb{R} \ w_1,\dots,w_i\quad w_i \geq 0 $$

  • Mean and variances

A naive approach to calculating a weighted streaming mean, $\widehat{\mu}$ and unbiased streaming variance, , would be to calculate:

$$ \begin{align*} \widehat{\mu}&=\frac{T^{(n)}}{S^{(n)}} \ \widehat{\mathbb{V}}&=\frac{n}{(n-1)S^{(n)}}\left(U^{(n)}-S^{(n)}\widehat{\mu}^2\right) \end{align*} $$

where

$$ \begin{align*} S^{(i+1)}&=S^{(i)}+w_i\ T^{(i+1)}&=T^{(i)}+w_i y_i\ U^{(i+1)}&=U^{(i)}+w_i y^2_i \end{align*} $$

This calculation however does not hold for large $n$ values.

An alternative calculation was suggested by West1, where we calculate:

$$ \begin{align*} \widehat{\mu}&=\frac{\sum_i w_i y_i}{\sum_i w_i} \ \widehat{\mathbb{V}}&=\frac{\sum_i w_i(X_i-\mu)^2}{\frac{n-1}{n}\sum_i w_i} \end{align*} $$

Let’s look at an example in Python.

from plotutils import *

import numpy as np

mu = 10.0
sigma = 20.0
N = 100_000

Y = np.random.normal(loc=mu, scale=sigma, size=N)

import matplotlib.pyplot as plt
import matplotlib

matplotlib.rc('figure', figsize=(12, 6))
plt.hist(Y, bins=50,color="lightpink")
plt.show()

As expected, the mean and variance when calculated in “batch” mode should be equivalent to the original values $\mu$ and $\sigma$.

print(f"mean: {np.mean(Y)}")
print(f"std: {np.std(Y)}")

mean: 10.040100563607174
std: 19.946298541957912

class StreamingStatistcs:
    def __init__(self):
        self.sum = 0.0
        self.mean = 0.0
        self.t = 0
        self.n = 0
        self.var = None

    def calculate(self, y, w):
        q = y - self.mean
        tmp_sum = self.sum + w
        r = q*w / tmp_sum

        self.mean += r
        self.t += q*r*self.sum
        self.sum = tmp_sum
        self.n += 1
        if (self.sum == 0.0 or self.n < 2):
            self.var = 0
        else:
            self.var = (self.t*self.n)/(self.sum*(self.n-1))
        return (self.mean, self.var)

means = []
vars = []
st = StreamingStatistcs()
for y in Y:
    m, v = st.calculate(y, 1.0)
    means.append(m)
    vars.append(np.sqrt(v))

fig, (ax1, ax2) = plt.subplots(1, 2)
fig.suptitle('Streaming statistics for $N=10^5$ observations')
ax1.plot(means)
ax1.hlines(y=10, xmin=0, xmax=N, colors="red")
ax1.set_title("Streaming mean")
ax2.plot(vars)
ax2.hlines(y=20, xmin=0, xmax=N, colors="red")
ax2.set_title("Streaming variance")
plt.show()

  1. cite:&west1979updating West, D. (1979). Updating mean and variance estimates: an improved method. Communications of the ACM, 22(9), 532–535. ↩︎