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Unsynchronized time series visualization

Data Science Asked on April 21, 2021

I would like to visualize a large amount of events composed of time serie windows. A typical event would be:

typical event

Problem is, my events are not synchronized, and so if I plot them all, it would look like:

enter image description here

Question
Is there any way to visualize all my events so I can see their original/"typical" shape (preferably in the time domain) despite their unsynchronization ?

What I have tried so far:

  • Visualize features: approach is good but I have to guess what I am looking for.
  • Synchronized events: in the example above it might be possible, but for some other cases it won’t be. (such as multiple peaks spaced with different time lapses)
  • Use statistical values to visualize: that was my first shot, to plot not all events but only their mean, median, p95,… Problem is, using this approach gives a deformed rendering of the data because of the unsynchronization:

enter image description here

What I have thought about

The main problem of applying the mean, p95 or whatever statistical function is that it is applied along an axis that is not synchronized between events. I don’t know if it exists or if it is even feasible, but I was thinking about an approach that would display those statistical values checking their temporal neighbors ? I know Dynamic Time Warping deals with this unsynchronization but I am not sure if and how I could use it to plot my events in the way I’d like.
An idea of a possible output could be (any other output idea is welcomed!):

enter image description here

Any help, idea, would be greatly appreciated ! Thanks

One Answer

As far as estimating similarity between the time series series there is a variety of methods you may want to investigate. Some of those are:

  • cross correlation: this will be affected by the amplitude and will not be able to estimate lagged correlations, prone to noise.
  • coherence: normalised frequency based correlation (cross-spectrum), not prone to amplitude or noise.
  • wavelet coherence: similar to above but based on wavelet transformations instead of STFFT.
  • dynamic time warping: measuring similarity between two temporal sequences, which may vary in speed.

Answered by hH1sG0n3 on April 21, 2021

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