The unreasonable difficulty of time series forecasting

Posted by suzyahyah 3 days ago

The unreasonable difficulty of time series forecasting(suzyahyah.github.io)

98 points | 38 commentspage 2

ninju 1 hour ago||

hintymad 5 hours ago||

The examples in the posts suggest that the past does not contain all the patterns, or information in general, about the future. If so, isn't it natural that point forecast will fail in some cases?

pishpash 2 hours ago|

The past cannot contain all information about the future, or there is some superdeterminism going on, by definition.

BrokenCogs 5 hours ago||

Not at all unreasonable, why should predicting the future be easy for those of us living in the simulation?

xnx 6 hours ago||

"It is difficult to make predictions, especially about the future."

joe_the_user 2 hours ago||

I remember in 2017, it was said that "regular" machine learning techniques were better for time series than neural networks. It seems like this blog post is indirectly references that belief and gives. It seems like the idea is simpler ML can be tuned to a given time series whereas neural network require lots of training.

Also, one method works better than another is more meaningful than "predicting the future is hard".

joe_the_user 5 hours ago|

The question "what probability distribution generated this" is very hard to answer if you're only getting short window before the distribution changes. A lot of distributions could generate relatively "short" set dispersed data points and deciding which distribution is "really" creating the points might not even be meaningful. A distribution is a mathematical device, not something with a physical existence.

mjburgess 5 hours ago|

Probability distributions don't generate data, they describe our uncertainty about its generation retrospectively. So there is no answer to that question. The lack of such an answer is at the heart of why such inference is hard: in most cases, there isnt a stable underlying reality of anything doing the distributing.

joe_the_user 3 hours ago||

I think we're saying roughly the same thing. It's natural starting with a statistical model to say it generates data (as the article sometimes does) but in reality, you often/generated have data generated by a possibly deterministic and completely different process but satisfying (if you're lucky) the statistical conditions. Even in testing random/stochastic models, you would usually use pseudo-random generators - which are deterministic.