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I'm intending to add white noise to a simple periodic signal

   p = TransformedProcess[
         Cos[t/8] + noise[t],
         noise \[Distributed] WhiteNoiseProcess[],
         t];

Adding it at integer intervals is fine

   data = RandomFunction[p, {0, 10}]

yields a TemporalData value as expected

But

   data = RandomFunction[p, {0, 10, 0.1}]

causes an error

"The specification WhiteNoiseProcess[NormalDistribution[0,1]] is not a random process recognized by the system"

Am I at fault or is it a bug?

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The issue is to do with RandomFunction

For continuous-time processes with jumps, such as WhiteNoiseProcess[] the step dt is random and given by the process itself.

e.g this works

RandomFunction[WhiteNoiseProcess[1/3], {0, 50}]

This doesn't

RandomFunction[WhiteNoiseProcess[1/3], {0, 50,0.1}]

For continuous-time processes without jumps, such as WienerProcess an explicit dt needs to be given.

e.g this works

RandomFunction[WienerProcess[], {0, 50, 0.1}]

this doesn't

RandomFunction[WienerProcess[], {0, 50}]

You can't mix the two.

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  • $\begingroup$ thanks, that with reference.wolfram.com/language/ref/TimeSeriesRescale.html will meet my needs. Just makes me ponder about the real nature of white noise. Is there a better solution to modelling acoustic noise than white noise? The time series I’m modelling in the end being an acoustic one. $\endgroup$ – Nick Jan 2 '20 at 18:47

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