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

first-steps
time-series

#1

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?


#2

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


#3

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.


#4

Sounds like an idea for a new package? :laughing:

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


#5

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


#6

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


#7

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.


#8

if times is what you do then yeah.


#9

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.