I have a time series defined by gradual changes interrupted by large jumps; the time series also contains random noise. It also has irregular time steps. With my eye, I can separate the time series into uniform segments of the gradual changes and ignore the noise. I would like to automate this as I actually have several hundred such time series. Are there existing techniques to do this kind of task? Otherwise I will trial and error criteria to break the time series into segments, smooth down the noise, then differentiate. My attempts have not matched my eye's results so I want to make sure I am not missing any known approaches.