Speaking (very) roughly, the fact that they are autoregressive constrains how 'kinky' the shapes can be. Visually speaking, the time series will have a small number of inflection points and will appear interpolated between these points.
This greatly reduces the search space: you just need to line up a couple of kinks in a space of ten samples, or match two smooth and vaguely line-shaped objects, rather than find a convincing relationship for 100s of observations not tied together in time.
[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.611...
minutephysics Correlation CAN Imply Causation! | Statistics Misconceptions
My height has increased over the last century.
How many things on that first list are caused by my increasing height? How many caused my increasing height?
- Calories available
- Vaccination rates
- Access to neo-natal care
- Knowledge about fetus and infant development
Correlation is insufficient to prove causation but in many cases it's a great hint.
I'm gonna need a new wardrobe...
You've confused an individual trend with a trend of the aggregate maximum value.
A couple of those may have a minor influence on the limit of my height, but increasing those factors has no effect on my height.
Or how about my age?
And don't forget other highly correlated values, like the number of movies or books published, Chinese population, cumulative deaths in war, and number of artificial satellites. There's a correlation of my age (and height) with each of those.
Just two temporal trends that move in the same direction.
No causation, though.