Realist: That’s a solid idea if you can define the noise precisely and get consistent metrics. Start by quantifying the variance of the incoming data, then set thresholds for when a spike is statistically significant. Make sure you have a clear trigger for when a new signal is considered “burst.” Data will tell you if the approach works.

Sounds practical. Just make sure the threshold is based on real historical variance, not just a guess. Use a rolling window to calculate standard deviation, then set a multiplier for the spike trigger. Also keep an eye on false positives—if the signal screams too often you’ll lose trust. Once you confirm it’s reliable, go ahead.