I've obtained 2840 observations of a stochastic process with large Gaussian noise. The process parameters change at one point around the 1500th observation. I've ensemble-averaged these into sets of 10, 40 and 284 observations and would like to present those three sets to the readers of my report, but I can't figure out a good way of visualising this data.
Ideally, the visualisation would let the viewer see:
- the wave morphology
- the impact of noise, i.e. how much one averaging differs from another in the set
- the point where the wave morphology changes
I could plot them all in a single graph. This gives a good idea about the impact of noise, but loses the chronological order of the waves. Also, it would only work for the set of 10 before becoming unmanageable.
I could also plot them as a 3D surface with the X axis showing time in a sample and the Z axis the ordinal number of the observation. One problem is that some waves are seen better than others, making it hard to compare the noise levels and wave morphologies quantitatively from the graph.
How could I best visualise these averaged sets of observations?