# Exponentially weighted moving average

Hi there,

I understand this is a nerdy one, but…

Is there a way to get exponentially weighted moving average of intervals different from 42 and 7 days? Bith in fitness and activity charts?

Any workaruond in Java maybe?

Thanks!

Riccardo

I’m not sure if this is what you need but you can set the days for fatigue and fitness

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Thank you for your reply… no, it’s not really what I need.

I’d like to have plots for exponentially weighted moving average o whatever data set (e.g. weight), where I can set the period as I like (other than 7 or 42).

Formula/explanation here:

https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc431.htm

Thanks!

Do you mean like this?

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Not sure if you need exponential average or just simple moving average.
It seems you want to evaluate average for last N days, so it is given by N points moving average.
Exponential average doesn’t have a finit period of time associated, but a factor that reduces relevance of past values, but it takes into account full time series

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Exponentially weighted moving average is widely used in data analisys and is different from simple moving average as it assigns different “weights” to past data depending on their position back in time (in simple moving average, all data in the set have the same “weight” equals to 1, i.e. every data back in time have the same impact on the final result).

In th esimplest form of EWMA the “weight” or smoothing factor depends on how large is the interval you’re looking at, i.e. it is a function of it. Tipically, a data will have less and less influence on current value as it goes further back in time.

It’s the same principle of fitness and fatigue plots, but instead of having 42 and 7 days long intevals, you can set the interval as you like. One widely used interval is 20 (~0.9 smoothing factor), which has proved to have some sense in real life when you are predicting/controlling complex systems. It comes in handy, for example, for body weight tracking.

Thanks!

Riccardo

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Nope, see my explanation just above.

Thanks!