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/

[stared → dia80]

I second for PyMC3 - a great library. And just recently I went through 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...

[TheLogothete → liviu-]

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)

[huac → dandermotj]

or

  library(pacman)  
  pacman::p_load("lubridate", "bts", ...)

with the added benefit of installing missing packages (I find this especially useful because my school's computer lab deletes user-installed packages weekly)

[GFK_of_xmaspast → dandermotj]

That doesn't seem to me to be a huge efficiency win.

[dandermotj → GFK_of_xmaspast]

In terms of managing packages in session, readability and keystrokes, I think it definitely wins out over successive library calls. It's common to have >5 packages in any one script, especially if you avoid base R like many do.

[RockyMcNuts → dandermotj]

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 → RockyMcNuts]

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.

[tanlermin → RockyMcNuts]

Check out rcall.jl

[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?

[kgwgk → hoodwink]

"Statistical Rethinking" by Richard McElreath is really nice, light on maths but heavy on insights. He uses the book in a course and there are recordings of the lectures available on youtube.

http://xcelab.net/rm/statistical-rethinking/

Edit: neither this book nor Kruschke's are going to help you with time series in particular. Bayesian time series methods are based on state space models and are relatively complex (http://www.eurasip.org/Seminars/Tutorials/EUSIPCO2014%20Tuto...). You might want to read some introductory text on time series covering state space models first.

Edit again: The full text of "Bayesian Filtering and Smoothing" by Simo Sarkka is available online: http://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111....

[fergal_reid → hoodwink]

Having read many books/online courses, I strongly recommend this: http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp...

It gives a gentle but through introduction from first principles; lots of good intuition and 'why'.

It works well with "Probabilistic Programming & Bayesian Methods for Hackers" also mentioned, but I'd start with this. It is much more accessible than many other introductory books, IMO.

[alex_hirner → hoodwink]

http://camdavidsonpilon.github.io/Probabilistic-Programming-...

[dandermotj → alex_hirner]

Another open source Bayesian book/course all in python: http://www.greenteapress.com/thinkbayes/t

[pjscott → dandermotj]

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

http://greenteapress.com/wp/think-bayes/

[tmptmp → pjscott]

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

[fludlight → hoodwink]

http://ocw.mit.edu/courses/electrical-engineering-and-comput...

[hoodwink → fludlight]

Thank you!

[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 → MrQuincle]

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...

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 → tomrod]

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 → chestervonwinch]

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

[FreeFull → tomrod]

There definitely isn't sufficient contrast.

[banku_brougham → tomrod]

I liked it on iPhone 6 plus

[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 → uptownfunk]

Short: http://a-little-book-of-r-for-time-series.readthedocs.org/en... or 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/

[dandermotj → stared]

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.

[em500 → uptownfunk]

Hyndman's online forecasting book https://www.otexts.org/fpp has an ARIMA section that covers pretty much everything a practitioner would need.

[bachmeier → uptownfunk]

Try one of the forecasting books on Frank Diebold's page: http://www.ssc.upenn.edu/~fdiebold/Textbooks.html

I use the Elements of Forecasting book in an undergrad forecasting class that I teach. Forecasting will be better, but I'm not sure if it's completed.

[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.)