Many of these timeseries filters are really useful for robotics — but only if they can accept new inputs.
For example, Kalman filters are used to interpret GPS data in turn-by-turn mapping software. Given this data from the iPhone accelerometers, and these map constraints, where am I likely to be located?
But that requires the filter to be able to update with new information. How would we accomplish this in Statsample-Timeseries as currently written? Can it already be done?
Kalman filters can actually incorporate any kind of data — sensors, gravity, thrust, etc. For this purpose they're often used in spacecraft navigation. Here, too, they need to be able to accept new inputs.
Many of these timeseries filters are really useful for robotics — but only if they can accept new inputs.
For example, Kalman filters are used to interpret GPS data in turn-by-turn mapping software. Given this data from the iPhone accelerometers, and these map constraints, where am I likely to be located?
But that requires the filter to be able to update with new information. How would we accomplish this in Statsample-Timeseries as currently written? Can it already be done?
Kalman filters can actually incorporate any kind of data — sensors, gravity, thrust, etc. For this purpose they're often used in spacecraft navigation. Here, too, they need to be able to accept new inputs.