3 ms·
ARIMA models, seasonal adjustments,... this is still largely based on the Box-Jenkins method (developed in the 70's!). I feel like this stuff has been taught th
by t_mann 3y ago
ARIMA models, seasonal adjustments,... this is still largely based on the Box-Jenkins method (developed in the 70's!). I feel like this stuff has been taught the same way for decades now (maybe similar to undergraduate classical mechanics or other topics that are considered 'solved'). Is this really still the state of the art? Time series analysis seems oddly close to machine learning, which seems to move at break-neck speed all the time, yet it feels completely stuck in time. Can someone unravel that paradox for me?
- lukego 3y agoMaybe of interest: https://github.com/probsys/AutoGP.jl https://github.com/probsys/AutoGP.jl
- disgruntledphd2 3y agoTS models are constrained by the realities of the world they exist in. While you can chase benchmarks in lots of ML problems, forecasting is something that's used by basically every large business with huge consequences to getting it wrong or right. Therefore, people stick with relatively performant & interpretable methods such as ARIMA and friends. Additionally, most TS problems are relatively data constrained (your company/product has only existed for so long) so methods that are sample efficient (which most "modern" ML methods are not) are much more useful. Also, time series/forecasting is a ghetto ;)
- boppo1 3y ago>time series/forecasting is a ghetto ;) What do you mean?
- disgruntledphd2 3y agoIt's very very disconnected from overall modelling, with its own approaches and culture. There's little to no cross pollination from other areas of modelling.
- bradstewart 3y agoNew things are coming out, Meta's "prophet" for one. I've been pretty impressed with it in a "just throw data and don't even think about parameters" sense. But the fact is, ARIMA models work. So people keep using them. And you can see what they're doing, and understand why, and how to tune them.
- nerdponx 3y agoProphet isn't necessarily new either. It's "just" a linear model, with some specially-crafted features that work well for the specific problems that Facebook/Meta had at the time when they built it. It's also not considered a good general default choice for time series forecasting, but that's another story.
- Terretta 3y ago“Hari Seldon is a fictional character in Isaac Asimov's Foundation series. In his capacity as mathematics professor at Streeling University on the planet Trantor, Seldon develops psychohistory, an algorithmic science that allows him to predict the future in probabilistic terms.” The problem isn't the math, the problem is people are not rational.