How can I (efficiently) compute a moving average of a vector?

Question

I've got a vector and I want to calculate the moving average of it (using a window of width 5).

For instance, if the vector in question is [1,2,3,4,5,6,7,8], then

• the first entry of the resulting vector should be the sum of all entries in [1,2,3,4,5] (i.e. 15);
• the second entry of the resulting vector should be the sum of all entries in [2,3,4,5,6] (i.e. 20);
• etc.

In the end, the resulting vector should be [15,20,25,30]. How can I do that?

Answer 1

The conv function is right up your alley:

>> x = 1:8;
>> y = conv(x, ones(1,5), 'valid')

y =
    15    20    25    30

Benchmark

Three answers, three different methods... Here is a quick benchmark (different input sizes, fixed window width of 5) using timeit; feel free to poke holes in it (in the comments) if you think it needs to be refined.

conv emerges as the fastest approach; it's about twice as fast as coin's approach (using filter), and about four times as fast as Luis Mendo's approach (using cumsum).

Here is another benchmark (fixed input size of 1e4, different window widths). Here, Luis Mendo's cumsum approach emerges as the clear winner, because its complexity is primarily governed by the length of the input and is insensitive to the width of the window.

Conclusion

To summarize, you should

• use the conv approach if your window is relatively small,
• use the cumsum approach if your window is relatively large.

Code (for benchmarks)

function benchmark

    clear all
    w = 5;                 % moving average window width
    u = ones(1, w); 
    n = logspace(2,6,60);  % vector of input sizes for benchmark
    t1 = zeros(size(n));   % preallocation of time vectors before the loop
    t2 = t1;
    th = t1;

    for k = 1 : numel(n)

        x = rand(1, round(n(k)));  % generate random row vector

        % Luis Mendo's approach (cumsum)
        f = @() luisMendo(w, x);
        tf(k) = timeit(f);

        % coin's approach (filter)
        g = @() coin(w, u, x);
        tg(k) = timeit(g);

        % Jubobs's approach (conv)
        h = @() jubobs(u, x);
        th(k) = timeit(h);
    end

    figure
    hold on
    plot(n, tf, 'bo')
    plot(n, tg, 'ro')
    plot(n, th, 'mo')
    hold off
    xlabel('input size')
    ylabel('time (s)')
    legend('cumsum', 'filter', 'conv')

end

function y = luisMendo(w,x)
    cs = cumsum(x);
    y(1,numel(x)-w+1) = 0; %// hackish way to preallocate result
    y(1) = cs(w);
    y(2:end) = cs(w+1:end) - cs(1:end-w);
end

function y = coin(w,u,x)
    y = filter(u, 1, x);
    y = y(w:end);
end

function jubobs(u,x)
    y = conv(x, u, 'valid');
end

---

function benchmark2

    clear all
    w = round(logspace(1,3,31));    % moving average window width 
    n = 1e4;  % vector of input sizes for benchmark
    t1 = zeros(size(n));   % preallocation of time vectors before the loop
    t2 = t1;
    th = t1;

    for k = 1 : numel(w)
        u = ones(1, w(k));
        x = rand(1, n);  % generate random row vector

        % Luis Mendo's approach (cumsum)
        f = @() luisMendo(w(k), x);
        tf(k) = timeit(f);

        % coin's approach (filter)
        g = @() coin(w(k), u, x);
        tg(k) = timeit(g);

        % Jubobs's approach (conv)
        h = @() jubobs(u, x);
        th(k) = timeit(h);
    end

    figure
    hold on
    plot(w, tf, 'bo')
    plot(w, tg, 'ro')
    plot(w, th, 'mo')
    hold off
    xlabel('window size')
    ylabel('time (s)')
    legend('cumsum', 'filter', 'conv')

end

function y = luisMendo(w,x)
    cs = cumsum(x);
    y(1,numel(x)-w+1) = 0; %// hackish way to preallocate result
    y(1) = cs(w);
    y(2:end) = cs(w+1:end) - cs(1:end-w);
end

function y = coin(w,u,x)
    y = filter(u, 1, x);
    y = y(w:end);
end

function jubobs(u,x)
    y = conv(x, u, 'valid');
end