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
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.
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:
- Use the characteristics of the signal in a given time window and construct an n dimensional vector.
- You could use similarity measures or distance measures like cosine, manhattan etc to evaluate the similarity between them.
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.