
Forecasting at Uber: An Introduction - kiyanwang
https://eng.uber.com/forecasting-introduction/
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hef19898
Very interesting post, especially with regards to the granularity Uber needs
to forecast. This granularity on the time scale alone means that Uber is kind
of forced to compress a pretty big part of there Sales and Operations Planning
into a very short period of time.

I'm really looking forward to the follow up posts, espiacially the process is
intriguing. Being an Ops guy myself I aleeady see now a lot of challenges and
also fun in this.

Also nice note: Traditional taxis don't have that problem (in a positive
sense). Having a learning and intelligent network of drivers reacting tobthese
paterns and acting them allows them to achieve availability, in less efficient
and less customer friendly way. Another advantage taxis is fixed pick-up
locations (at least they exist in Germany). This allows traditional taxis to
shape demand on the location level. By provding cuatomers a more convenient
solution Uber now is forced to actively plan on both axis.

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JepZ
OT: I don't want to make fun of the authors, but I kinda had a laugh when I
saw this code next to the authors pictures:

    
    
      <div id="sexy-author-bio" style="margin:10px 0;" class="slawek-smyl">
    

Maybe just not my type ;-)

Some frontend developers should really care more about the identifiers they
use...

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tomnipotent
It's a WordPress plugin.

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QML
Does anyone know if uber or another on demand company has a similar type blog
post, but about predictive matching? Algorithms like stable marriage are
fascinating from a theoretical standpoint but I would love to hear about them
from people who implement or deploy them!

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gwern
I'm a little amused that their forecast visualization seems to repeatedly show
substantial ride demand in the middle of the bay. Kayakers getting tired and
hailing a ride?

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cbhl
I wonder if there are people who request a Uber from the commuter ferries
right before they're about to dock.

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Rainymood
Interesting article but rather shallow. Still funny to see Uber also uses
classical algorithms and some ML here and there, I mean, what did I expect
otherwise? Probably some super secret algos no one would know about ... nope,
just classical econometrics time series analysis and some ML sprinkled in
there.

Gives me hope I could work there one day as an econometrics graduate :)

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natalyarostova
You absolutely could. I don't work at uber but work at another Big Tech
company on a similar team. We have plenty of econometrics grads on our team.
Forecasting is pretty resilient to hype methods, since extrapolating into the
future is so hard, there is rarely some treasure trove of data to throw into
an ML model to predict the future. The biggest drivers of uncertainty in these
problems tend to be unforecastable, so data-hungry models don't offer much
(depends on the horizon and specific problem, but this is true as a general
rule).

I'd say my only observation has been economists who _also_ meet the bar as
software engineers are a force to be reckoned with.

