# State of the art method(s) to find zero mean portions of a time series

I have noisy time series which I need to segment into those portions with a zero mean and those portions without a zero mean. Finding the boundaries as accurately as possible is important (clearly where the boundary precisely lies is a bit subjective). I think a cusum variant could be adapted to do this but as cusum is primarily about finding single changes that leaves the whole segmentation strategy completely unaddressed.

I'm sure a bunch of research has been done on this problem but have not been able to find it.

P.S. The amount of data in these time series is quite large, i.e. up to hundreds of millions of samples, and an individual sample can be a vector with a couple of hundred components, so a method that can be computed reasonably quickly is a significant factor.

P.P.S There isn't a segmentation tag, hence the classification tag.

mu(R,T)=w1*Sample(R,T)+w2*Sample(R,T-1)+w3*Sample(R,T+1)....