Learn How to Calculate SNR for Vector A in Time Series | Tips & Tricks

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The discussion revolves around calculating the signal-to-noise ratio (SNR) for a time-varying vector 'A' compared to a baseline 'B'. The signal 'S' is defined as the average deviation of 'A' from 'B', calculated using a specific formula that separates 'A' into two groups based on whether their values have increased or decreased. The noise is defined as the standard deviation of 'S'. There is uncertainty regarding the appropriate method to calculate SNR in this context, with a suggestion that dividing 'S' by its standard deviation could yield a meaningful SNR. However, clarity on the formula for 'S' is needed due to some missing brackets in the original equation.
sue132
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Hi,

I have a vector 'A' of length 'n', whose values change with time. 'A' gives the change from a baseline 'B'. I calculate a signal as the distance A from the average of B, i.e.,

If 'B' is my baseline, then the signal at a time 't' due to A(t) would be given by S=(1/n)*(Ʃ1Ai -<B> - Ʃ2Ai -<B>), i=1 to n. 'A' is divided into two groups of elements whose values are higher than their previous values and those whose values are lower.

I define noise as the standard deviation of S.

Given this, how do I define the signal-to-noise ratio of A?

I found different ways of calculating the SNR in different contexts, but am not very clear as to which one to use in this context.

Thanks in advance for any suggestions/help.
 
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Dividing S by the standard deviation of S should give a meaningful quantity, if the formula for S itself is meaningful (it has missing brackets here, by the way).
 
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