# Need advice on change point (step) detection

I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to the baseline of the signal (i.e. remove the jumps so I get a smoothly varying signal). A median filter works really well for removing a small number of outliers in a row, but in this case I probably need a different approach since the square wave jumps can have different durations as seen. A common method I've seen for doing this is to compute first differences of adjacent samples, and look for large differences to detect jumps. I implemented this method but the problem is it often fails, since the one tunable parameter for the method is a threshold value $t$ which the first differences must cross in order to detect a jump: $$| x_{i+1} - x_i | > t$$ As can be seen in the plot below, the jumps I have are often different sizes, so a constant threshold value isn't the best approach. In particular, in some cases there is an interesting signal where adjacent samples can change by large values without being a jump! I have highlighted such a region in red.

Below is a zoomed in view of the red box area. You can see there is a square wave jump followed by an interesting signal. The red arrows depict a place where adjacent samples from an interesting signal have a larger distance between them than some of the jumps in the signal. Therefore a constant threshold method with finite differencing will not work for me.

Does anyone know of a robust procedure to detect and subtract the square wave jumps to end up with a smoothly varying signal with no jumps? I'm sure this must be a solved problem but I haven't had much luck searching online.

• I might be in a very small minority here but I have never understood how one could create a procedure to find oddities (jumps, outliers, etc.) without first defining very specifically as to what an oddity is. In other words, I don't think a criterion of "I'll know it when I see it" is very useful or consistent. So...I think a specific definition for the types of oddities of interest along with a model as to how the data is generated and/or collected is necessary to proceed. – JimB Feb 17 '18 at 21:21
• In this case, I would like to detect and correct square-wave type jumps/steps in my time series. In other words there is an abrupt change in the mean of the time series for some amount of time, and then it returns to the previous baseline. – vibe Feb 19 '18 at 1:52
• reminds me of this thread: stats.stackexchange.com/questions/1142/… – Taylor Feb 19 '18 at 2:31

This is not exactly an answer, but an attempt to illustrate further the problems I'm having with the first differences method. In the figure below, I focus on that same region as in the original question. I have added a median filtered curve in green (7-point centered window), plus the curve of first differences of the median filtered curve in red ($x_{i+1}-x_i$). Finally in blue I plot a constant "threshold" value of $\pm 4$ to identify a step.
You can see that the first difference curve (red) identifies the step jump quite well, but around 30:00 the red curve also passes the threshold value when in fact there is no jump there. I cannot increase the threshold value any more because of the following situation a little later in the time series: Here you can see two step features in a row whose first differences don't quite pass the $\pm 4$ threshold. So these jumps are not correctly identified. So the question becomes how to "enhance" the first difference signal in the presence of a step, while reducing it in non-step cases like in the first figure above. One idea I have is to compute the median absolute deviation (MAD) of the previous N samples, and compare the first difference value with the MAD instead of using a constant threshold. But I haven't gotten this method to work very well either, because sometimes the MAD is very small, and even a modest first difference value would result in false positive.