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An <a href="http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average" rel="noreferrer">exponential moving average</a> is great for this. It provides a way to smooth your average so that each time you add a new sample the older samples become decreasingly important to the overall average. They are still considered, but their importance drops off exponentially--hence the name. And since it's a "moving" average, you only have to keep a single number around. In the context of measuring download speed the formula would look like this: <pre><code>averageSpeed = SMOOTHING_FACTOR * lastSpeed + (1-SMOOTHING_FACTOR) * averageSpeed; </code></pre> <code>SMOOTHING_FACTOR</code> is a number between 0 and 1. The higher this number, the faster older samples are discarded. As you can see in the formula, when <code>SMOOTHING_FACTOR</code> is 1 you are simply using the value of your last observation. When <code>SMOOTHING_FACTOR</code> is 0 <code>averageSpeed</code> never changes. So, you want something in between, and usually a low value to get decent smoothing. I've found that 0.005 provides a pretty good smoothing value for an average download speed. <code>lastSpeed</code> is the last measured download speed. You can get this value by running a timer every second or so to calculate how many bytes have downloaded since the last time you ran it. <code>averageSpeed</code> is, obviously, the number that you want to use to calculate your estimated time remaining. Initialize this to the first <code>lastSpeed</code> measurement you get.
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1. POHow to estimate download time remaining (accurately)?
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1. USBen Dolman
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1. COSimple, but looks good!
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