
Sorry ARIMA, but I’m Going Bayesian - stared
http://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/
======
dia80
Thanks for this. If you wanted to get started doing this kind of analysis in
python check out the examples for the maturing pyMC3

[https://pymc-devs.github.io/pymc3/stochastic_volatility/](https://pymc-
devs.github.io/pymc3/stochastic_volatility/)

~~~
stared
I second for PyMC3 - a great library. And just recently I went through
[https://github.com/markdregan/Bayesian-Modelling-in-
Python](https://github.com/markdregan/Bayesian-Modelling-in-Python).

------
liviu-
I found it quite interesting to compare HN and /r/statistics in terms of the
tone difference in the comments when discussing this article:
[https://www.reddit.com/r/statistics/comments/4fw1bn/sorry_ar...](https://www.reddit.com/r/statistics/comments/4fw1bn/sorry_arima_but_im_going_bayesian/)

~~~
TheLogothete
Because "data science".

------
dandermotj
This is a really interesting post. I have to say the data science team at
Stitchfix are clearly doing really good, __applied __work that is central to
their business. It 's so cool to see.

Here's a tip for any R users who read through the code and (like me) is pained
by repetition. Instead of using

    
    
        library(lubricate)
        library(bts)
        library(...)
    

Just use apply!

    
    
        packages <- c("lubricate", "bts", "...")
        lapply(packages, library, character.only = TRUE)

~~~
RockyMcNuts
it's clever but is it more readable?

these sorts of discussions, people who write blog posts about 'library' vs.
'require', kind of feel like 'R smell'.

wouldn't it be better for a language to just take a list of libraries for
import, maybe with a readable syntax?

so... have we got to where Julia can re-use R packages yet?

~~~
dandermotj
I understand where you're coming from, but these two lines are exactly 'taking
a list of libraries for import'.

packages is a character vector of package names, lapply is by definition 'list
apply'. We're taking a list of packages and applying the library function on
them.

This seems complicated if you're not used to it but _R is a functional
language_. Approaching R from this perspective makes it a powerful, flexible.

------
hoodwink
Like others, I enjoyed this.

I work with time series data every day in the domain of commercial real
estate. One of my constant struggles is to extract an underlying long-term
trend from the real estate cycle. I would love to try this here.

Can anyone suggest some Bayesian learning resources for a non-statistician?

~~~
alex_hirner
[http://camdavidsonpilon.github.io/Probabilistic-
Programming-...](http://camdavidsonpilon.github.io/Probabilistic-Programming-
and-Bayesian-Methods-for-Hackers/)

~~~
dandermotj
Another open source Bayesian book/course all in python:
[http://www.greenteapress.com/thinkbayes/t](http://www.greenteapress.com/thinkbayes/t)

~~~
pjscott
If you can program, this is the best probability book. I think you messed up
the URL, though:

[http://greenteapress.com/wp/think-bayes/](http://greenteapress.com/wp/think-
bayes/)

~~~
tmptmp
Thanks a ton you all posters of these book names and links. Helped a lot.

------
MrQuincle
Really nice story. All the Bayesian stuff now gets swamped by big data method.
There must be a time machine learning people will come back to the Bayesian
womb. :-)

I'm currently using it to define priors on measure spaces. I think it's
awesome to have so few abstractions in a discipline and be able to do
inference anyway. I'd definitely recommend to look into Dirichlet Processes if
you haven't before. It's a nice entry point.

~~~
huac
Wharton's Pete Fader would agree with you somewhat that big data-style methods
are overblown:
[http://www.datanami.com/2012/05/03/wharton_professor_pokes_h...](http://www.datanami.com/2012/05/03/wharton_professor_pokes_hole_in_big_data_balloon/)

he also has some very cool uses of dirichlet processes (modelling entire
competitive industries)

------
radicality
I was very surprised when I went to the root domain to see what stitchfix is.
Completely not what I was expecting! I guess my model didn't predict a clothes
delivery website to be writing about cool stuff like this. But now a bayesian
update to this belief is in order :)

------
tomrod
I like the content.

Does anyone else have a _really_ hard time reading the light gray-on-white
font?

~~~
chestervonwinch
It is ironic that your off-topic comment lead to down-votes, reducing the
contrast of your comment. I can't imagine the nightmare you must be living
right now.

~~~
tomrod
Mostly, I'm unconcerned about karma. But at present, the contrast is fine. I
suppose the vote count has increased.

------
uptownfunk
Excellent walkthrough I'll be sure to give this a try on my next project.

Any good references for an intro to the traditional arima models?

~~~
stared
Short: [http://a-little-book-of-r-for-time-
series.readthedocs.org/en...](http://a-little-book-of-r-for-time-
series.readthedocs.org/en/latest/src/timeseries.html) or
[http://people.duke.edu/~rnau/411arim.htm](http://people.duke.edu/~rnau/411arim.htm)

And for a longer, a book by the author of R forecast library:
[https://www.otexts.org/fpp/](https://www.otexts.org/fpp/)

~~~
dandermotj
This book is extremely practical - I would definitely recommend it for doing
actual time series analysis. That said, it's not a _learn time series book_.
It's a _do basic time series in R_ book.

------
toxik
This was very interesting to read, especially as I'm currently taking a course
in time series analysis. The course doesn't touch on any Bayesian treatment of
the topic at all, and barely goes beyond non-stationary models (e.g. ARIMA.)

