SideCar's Kalman Filter models San Francisco brunch

[gpcz]

Typically, a Kalman filter is only as accurate as your mathematical model of the underlying phenomena (for example, your flight dynamics on an airplane), and the filter is mainly useful to mitigate the noise from your real-world sensor observations. Is there more public information available about the mathematical model being used within their state transition matrix? Depending on that information, this could be really clever or a glorified low-pass filter.

[lnanek2]

But they say they are looking for people to go to brunch, wait in line, then return. That's two short spikes of activity next to each other once a day. A low pass filter would filter that out as a high frequency noise.

Even basic stuff like how long it takes someone to drive from one spot to the other before they can contribute to increased demand in Oakland when they are in SF seems more complex than a basic filter and in line with Kalman.

[ericwaller]

All you kalman filter fans out there will be happy to hear that you can grab a ride from SideCar and some Giants tickets from SeatGeek[1] for a truly algorithmic afternoon.

1. http://chairnerd.seatgeek.com/using-a-kalman-filter-to-predi...

[nullc]

Expected math. Was disappointed.

[dfc]

I thought all models literally model the subject that they model.

[caycep]

Not an expert in this field, but amusing to me in that the only other place i've had the fortune of encountering kalman filters were groups trying to analyze neuro data from Blackrock Utah micro electrode arrays.

Brain signals, brunch, they all look the same...

[tel]

They show up all over the place in time series and more general random signals. Those are just less than incredibly popular domains, in no small part I feel due to the greater tool sophistication needed to make a dent.

[spinlock]

I think it's more fun that they model SFers standing in line for brunch.