Hi.
On Mon, 16 May 2016 14:40:06 +0100, Daniel Winterstein wrote:
Hello,
I was doing some work with Kalman Filters recently, and I noticed the
implementation in commons.math is missing a couple of features,
namely
smoothing and model-fitting.
Would it be good to add these?
Sure.
A little bit more on the model and the proposed features:
The Kalman Filter is a model for time-series data.
The version in commons.maths can do prediction (i.e. given the state
now, predict the next state).
The algorithm can also do smoothing (given a sequence of data,
perhaps
with gaps, estimate the most likely state at each step) -- but this
version doesn't have an implementation.
Also, given a time-series dataset, you can fit a Kalman Filter. There
isn't a precise solution, but using expectation maximisation works
well.
I am new to the Apache Commons mailing list -- please forgive me if
this is the wrong way to go about things.
This is the right way.
All significant changes/additions to the code must be decided on here.
Then the implementation details can be discussed within a dedicated
issue
on the bug-tracking system:
https://issues.apache.org/jira/browse/MATH
Thanks in advance for your contributions,
Gilles
Kind regards,
- Daniel
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