Given a multivariate time series, which method could be applied to segment it into partitions of stationary signals.
To give more context: I am given a set of acceleration signals in XYZ-Direction. Based on these signals, I have to map a segment of the signal to a behaviour. The test-signals are not segmented.
So I have to find a way to segment it and apply standard classifier on it.
I have a similar problem and found out that Hidden Markov models work quite well.
But do you know the pattern of the behaviour you want to detect in advance? Or at least the segment duration? Because in that case you might also be able to use other techniques such as a sliding window with autocorrelation algorithm for example.
Dynamic programming techniques such as top-down or bottom-up algorithms (see: An online algorithm for segmenting time series) should provide an alternative solution too.