# TSAnalysis: time series analysis and state-space modelling

**URL:** <https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672>\
**Category:** Package Announcements\
**Tags:** statistics, time-series, machine-learning\
**Created:** [November 4, 2019, 1:04am UTC](https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672 "2019-11-04T01:04:45Z")\
**Posts on this page:** 1\
**Showing post:** 28

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**Author:** ![fipelle](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/fipelle/32/4772_2.png) [@fipelle](https://discourse.julialang.org/u/fipelle)\
**Post date:** [January 4, 2020, 2:29am UTC](https://discourse.julialang.org/t/tsanalysis-time-series-analysis-and-state-space-modelling/30672/28 "2020-01-04T02:29:33Z")

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> [@juliohm](#):
>
> I’ve just checked the TimeSeries.jl package, and it has some nice abstractions already. The type has many features along the lines of GeoStats.jl types. I would just contribute models to that package instead of trying to create a new package for time series.

I think we have to agree to disagree on TimeSeries.jl.

Often, regular arrays are enough to estimate time series models and forecast. More complicated structures are nice for EDA, but are superfluous for what I generally do. This is one of the reason why I prefer to have something simpler like TSAnalysis.jl (other reasons are above and [here](https://discourse.julialang.org/t/how-can-we-create-a-leaner-ecosystem-for-julia/32904/10)).

> [@markushhh](#):
>
> This looks great! I’ve been working on a GARCH modeling package which I haven’t released yet because I itend to do some breaking changes which would be very confusing in the short run.  
> I’d love to contribute to a general `TimeSeries.jl` package but I don’t know if I have time for it in the near future. Publishing the package such that all the functions can be copy pasted (and maybe improved if necessary) could be a contribution though. I have hopefully a quite good documentation right now as well.  
> Moreover, Simon Broda and his `ARCHModeling.jl` package might contribute a lot to this.  
> `TimeSeries.jl` seems to me to be the perfect fit for a name. My package would only be `GARCH.jl` …

It looks interesting. If you can try to follow a similar style of the ARIMA model in TSAnalysis.jl we could add it to the package. Of course, I perfectly understand if you prefer to keep it separate 🙂

> [@BLI](#):
>
> I think MATLAB’s N4SID algorithm works as an extension of Aoki’s work, where you find models
> 
> xt+1=Axt+But+Γwtyt=Cxt+Dut+vt x\_{t+1} = Ax\_t + Bu\_t + \Gamma w\_t \ y\_t = Cx\_t + Du\_t + v\_t
> 
> where the algorithm finds (A,B,Γ,C,D)(A,B,\Gamma,C,D) as well as the covariance of wtw\_t when the covariance of vtv\_t is assumed (?). Here, utu\_t is a deterministic input, while wtw\_t and vtv\_t are stochastic. Aoki worked with systems without deterministic input utu\_t – at least in his 1987 book.

It should be relatively easy to add exogenous predictors. I will add it to the to do list! 🙂

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