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Considering the data is received from a streaming source each second.How to distinguish if both the line graphs 'look' same/different in real time, statically, like the picture given below enter image description here


Edit:
1.Not sure if there is something as real-time periodic correlation mechanism then cross-correlation would perhaps be an ideal solution ?
2. Comparing slope of the two Line is the last option I would go with. 3. If statistically there is no way to solve this then I would look at machine learning to solve this

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3 Answers 3

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Maybe it is a huge oversimplification, but you can try: What if you do just arithmetic difference between the two signals (or divide one signal to another one) and look at the result?

I would expect that you will have spikes where signals become very different and you can make a threshold to find where they are on time.

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I am unsure on how to do this statistically apart from using the slope. However, there are some interesting ways to think about time series in a given time period to find similarity. You could do as follows:

  1. Use the characteristics of the signal in a given time window and construct an n dimensional vector.
  2. You could use similarity measures or distance measures like cosine, manhattan etc to evaluate the similarity between them.
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  • $\begingroup$ even with slope methods ,considering both the signals donot rise or fall by equal slopes , how to detect if they are similar? $\endgroup$
    – Rai Bose
    Commented Nov 15, 2018 at 18:47
  • $\begingroup$ I am not quite sure, maybe finding the average of the slope and comparing helps. Here is how you could find the average of the slope : searchink.atlassian.net/wiki/spaces/PROJ/pages/282329252/… I still think finding a vector representation and using other metrics/measures will help you evaluate this better. $\endgroup$
    – Nischal Hp
    Commented Nov 15, 2018 at 18:58
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be series1 = S and series2 = T

It would come from:

Tn - Sn-1 = Sn - Tn-1, then:

Tn - Tn-1
-------------- = 1
Sn - Sn-1

In this way, favorable cases will give 1 and unfavorable cases will show noise.

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  • $\begingroup$ can you elaborate this? $\endgroup$
    – Rai Bose
    Commented Apr 16, 2019 at 8:56
  • $\begingroup$ Of course, the only way to know that the graphs behave the same is by comparing with the previous data. $\endgroup$
    – Victor Villacorta
    Commented Apr 16, 2019 at 20:57

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