As developers, we often find ourselves working with collections of data in our projects. Whether it's stock price data, weather readings, or server response times, this data can often be very noisy - lots of extreme, short, jumps and falls!

In many cases, we aren't concerned so much with the latest value in the data series, as the overall trend or direction the data is moving in. Enter rolling averages!

When we add the rolling average to our quarterly temperature graph, we can see a definitive trend in the data.

Or when monitoring environmental data, such as temperature or air quality, recent data points may be more indicative of the current conditions than older data points.
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