# Is there a Julia package that can decompose a time series data into trend, seasonality and random?

**URL:** <https://discourse.julialang.org/t/is-there-a-julia-package-that-can-decompose-a-time-series-data-into-trend-seasonality-and-random/9518>\
**Category:** Statistics\
**Tags:** first-steps, time-series\
**Created:** [March 5, 2018, 8:29pm UTC](https://discourse.julialang.org/t/is-there-a-julia-package-that-can-decompose-a-time-series-data-into-trend-seasonality-and-random/9518 "2018-03-05T20:29:00Z")\
**Posts on this page:** 1\
**Showing post:** 23

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**Author:** ![viraltux](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/viraltux/32/15236_2.png) [@viraltux](https://discourse.julialang.org/u/viraltux)\
**Post date:** [December 25, 2020, 2:06pm UTC](https://discourse.julialang.org/t/is-there-a-julia-package-that-can-decompose-a-time-series-data-into-trend-seasonality-and-random/9518/23 "2020-12-25T14:06:29Z")

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I was also looking for one way to decompose time series in Julia so I implemented **STL** here:

> **[GitHub - viraltux/Forecast.jl: Julia package containing utilities intended...](https://github.com/viraltux/Forecast.jl)**
>
> Julia package containing utilities intended for Time Series analysis. - GitHub - viraltux/Forecast.jl: Julia package containing utilities intended for Time Series analysis.

The package also has a plot recipe that mimics the R output when plotting STL objects, however there are some differences to make it closer to the choices made in the original paper for the representation.

![co2](https://global.discourse-cdn.com/julialang/original/3X/1/0/10f30c3b720f7ccf43c104f7e67450e8c7c5472d.png).

Merry Xmas!

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