Calculate simple and exponential moving averages for data series
What is the Moving Average?
The moving average works like this: it computes simple, weighted or exponential moving averages over pasted number series - the workhorse of smoothing noisy data. Everything happens on your device - the input never leaves your tab unless the tool clearly states it performs a server-side lookup.
How to use the Moving Average
Enter the values the calculator asks for.
Double-check the unit or period next to each field (per month vs per year, cm vs m).
Read the result together with the breakdown shown beneath it.
Adjust one input at a time to compare scenarios side by side.
Features
Calculate simple and exponential moving averages for data series
Instant result, no page reload
Works in any modern browser, desktop or mobile
Free with no signup
Common mistakes to avoid
Mixing units or periods between fields
Rounding inputs before the final calculation
Treating estimates as guarantees for major decisions
Examples
Input
Result
Series 10, 12, 15, 11, 18 with window 3
SMA: null, null, 12.33, 12.67, 14.67
Best used for
Getting a reliable answer for moving average without installing an app
One-off tasks done in under a minute from any device
Checking results before committing them to something important
Working on a phone where desktop software is unavailable
Privacy
Calculations run in your browser. Enter only what the calculation needs.
Frequently asked questions
Which moving average should I pick?
SMA for a plain smoothed trend, EMA when recent points should matter more (it reacts faster but is noisier), WMA when you have an explicit weighting story.
Which moving average type should I use?
Simple for a plain windowed mean, weighted when recent points deserve more influence, and exponential when you want smoothing that reacts quickly to turns but never fully forgets old values.
How do I read the output alongside the raw series?
The averaged line sits inside your data with the first window-1 points unaveraged - compare the two lines to see exactly where trend and noise diverge.