I was trying to do some time series analysis. In R, there is a `decompose`

function that can decompose a time series into trend, seasonality, and random noise.

Is there an equivalent in Julia?

I was trying to do some time series analysis. In R, there is a `decompose`

function that can decompose a time series into trend, seasonality, and random noise.

Is there an equivalent in Julia?

https://github.com/baggepinnen/SingularSpectrumAnalysis.jl does this, but in a perhaps slightly different way than what you are looking for.

It’s not clear how to do it from the readme. Feels like it’s not beginner friendly yet and it requires much higher level of maths and skills to understand.

Sounds like an idea for a new package?

R implements holt winters method. This paper may be helpful.

Time for a time series person to step in! I thought Julia sells alot to wall st. And they do time series alot. My needs are met by R for now. Data so small that I can just do it without thinking about performance

I don’t think a package to run this regression in what makes them choose programming language, tbh Also, Julia doesn’t sell anything, Julia Computing does.

I asked a similar question a while ago, and apparently there isn’t any. I have implemented Hamilton (2017) for detrending, and plan to release it soon. I also came up with a simple multilevel model-based deseasonalizer that seems to work surprisingly well, but that is still experimental.

As I said in the other topic, most of these methods introduce spurious patterns, especially for the “trend”. Deseasonalizing with sophisticated algorithms (STL or X13-ARIMA-SEATS) is also prone to this, to a smaller extent. But of course they are OK for exploratory plotting, one just has to be aware of this.

My point is that they are probably able to build their own code base where something like this enters as a component, rather than relying on some public open source compromise. Of course if there’s a great solution out there they might use it, but it won’t hold them back.