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I want to use a Kalman filter to reduce the noise in a time series. The underlying process has no dynamic and therefore can be modeled as an AR(1) process like a random walk.

data = RandomFunction[ARProcess[{1}, 1, {0}], {1, 100}];

If I then use the same process for filtering, it results in an error:

KalmanFilter[ARProcess[{1}, 1, {0}], data]

 (*KalmanFilter::klmnest: KalmanEstimator failed for the given time series 
 process.*)

I assume this is somehow linked to the fact that the given AR process is not weakly stationary, but according to the literature this should not be a problem and there are many examples of Kalman filters used in such cases. For example, slide 42 in these lecture notes https://www.math.kth.se/matstat/gru/sf2943/kalmanlect.pdf . Is there another way?

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