# Smoothing Noisy Data using Moving Mean
**URL:** https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329
**Category:** General Usage
**Tags:** data, smoothing
**Created:** [July 26, 2021, 7:50pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329 "2021-07-26T19:50:39Z")
**Posts on this page:** 10
**Page:** 1
### Author:  [@sekarsum](https://discourse.julialang.org/u/sekarsum)
#### Post date: [July 26, 2021, 7:50pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/1 "2021-07-26T19:50:39Z")
Hi there,
Is there a function in Julia that is similar to MATLAB’s smooth() function.
MATLAB smooth function: `z = smooth(y, span, method)` where
`y` → input array;
`span` → span of moving average;
`method` → option between default moving average, lowess, loess etc.
THe result of this operation `z` is a vector of the same dimension as `y` which is basically a smoothening of the original array `y` .
---
### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [July 26, 2021, 7:52pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/2 "2021-07-26T19:52:08Z")
have you seen [RollingFunctions.jl](https://github.com/JeffreySarnoff/RollingFunctions.jl)? Seems to be close to what you want. But it doesn’t have out-of-the-box support for `loess` etc. It _does_ have a way to use a custom function, so maybe it’s easy to add on your own.
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### Author:  [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)
#### Post date: [July 27, 2021, 8:12am UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/3 "2021-07-27T08:12:05Z")
For LOESS,
[https://github.com/JuliaStats/Loess.jl](https://github.com/JuliaStats/Loess.jl)
---
### Author:  [@viraltux](https://discourse.julialang.org/u/viraltux)
#### Post date: [July 27, 2021, 9:17am UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/4 "2021-07-27T09:17:00Z")
If it’s just a moving average and you want the same exact results as MATLAB then the only thing to figure out is what strategy MATLAB uses to estimate the tails (AR models, … etc.)
If you just want a smoother then they all have pros and cons, e.g. some can interpolate some cannot.
Anyway, to add options to the ones above we also have a `Henderson moving average filter` here [GitHub - viraltux/Forecast.jl: Julia package containing utilities intended for Time Series analysis.](https://github.com/viraltux/Forecast.jl)
```julia
using Plots, Forecast
x = log.( collect(1:.1:11) .+ rand(101))
scatter(x)
plot!(hma(x,21),legend=nothing)
```
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### Author:  [@thompsonmj](https://discourse.julialang.org/u/thompsonmj)
#### Post date: [September 3, 2021, 5:38pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/5 "2021-09-03T17:38:19Z")
There’s no singular function (yet) that has parity with MATLAB’s `smoothdata()`, and I +1 for whoever would like to implement something like it!
This works for a moving average at least:
```julia
using NaNStatistics, Plots
y = randn(1000)
window_size = length(y)/100.0
z = movmean(y, window_size)
plot(y, linestyle = :dot)
plot!(z, linewidth = 2)
```
---
### Author:  [@lmiq](https://discourse.julialang.org/u/lmiq)
#### Post date: [September 3, 2021, 8:10pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/6 "2021-09-03T20:10:52Z")
If you just want to plot the data more smoothly, there is this one:
```julia
julia> using EasyFit
julia> x = rand(100);
julia> m10 = movavg(x,10)
------------------- Moving Average ----------
Number of points averaged: 11 (± 5 points)
Pearson correlation coefficient, R = 0.43442913764734875
Averaged X: x = [0.4737608452310648, 0.5121944744036446...
residues = [-0.3269117547107911, 0.40109456416787137...
--------------------------------------------
julia> m50 = movavg(x,50)
------------------- Moving Average ----------
Number of points averaged: 51 (± 25 points)
Pearson correlation coefficient, R = -0.014175383510694936
Averaged X: x = [0.5225594827160708, 0.5220326497692106...
residues = [-0.37571039219579705, 0.3912563888023054...
--------------------------------------------
julia> plot([x,m10.x,m50.x],linewidth=2)
```
---
### Author:  [@rafael.guerra](https://discourse.julialang.org/u/rafael.guerra)
#### Post date: [September 3, 2021, 10:17pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/7 "2021-09-03T22:17:52Z")
For a package-free moving average, see @tim.holy’s solution [here](https://julialang.org/blog/2016/02/iteration/#writing_multidimensional_algorithms_with_cartesianindex_iterators), adapted as follows for `m` odd integer \> 1:
```julia
function moving_average(A::AbstractArray, m::Int)
out = similar(A)
R = CartesianIndices(A)
Ifirst, Ilast = first(R), last(R)
I1 = m÷2 * oneunit(Ifirst)
for I in R
n, s = 0, zero(eltype(out))
for J in max(Ifirst, I-I1):min(Ilast, I+I1)
s += A[J]
n += 1
end
out[I] = s/n
end
return out
end
```
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### Author:  [@tim.holy](https://discourse.julialang.org/u/tim.holy)
#### Post date: [September 4, 2021, 7:07am UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/8 "2021-09-04T07:07:52Z")
ImageFiltering.jl has multidmensional array smoothing. There’s also a `mapwindow(f, A, window)` which allows you to apply `f` to “windows” of `A` rather than the single elements that `map(f, A)` applies `f` to.
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### Author:  [@joshday](https://discourse.julialang.org/u/joshday)
#### Post date: [September 9, 2021, 8:24pm UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/9 "2021-09-09T20:24:33Z")
I’ll also note that an exponentially weighted mean is similar to a moving window and is easy to use via OnlineStats:
```julia
o = Mean(weight = ExponentialWeight(.1))
exp_smooth = [value(fit!(o, yi)) for yi in y]
```
---
### Author:  [@ayushpatnaikgit](https://discourse.julialang.org/u/ayushpatnaikgit)
#### Post date: [November 15, 2021, 9:54am UTC](https://discourse.julialang.org/t/smoothing-noisy-data-using-moving-mean/65329/10 "2021-11-15T09:54:50Z")
Hi,
How do you choose the optimal window size?
The package is very promising, I hope it develops quickly, I am looking forward to using it soon.
Many thanks,
Ayush