Many problems boil down to separating signal from noise, though what is “signal” and what is “noise” depends on context. One person’s signal is another person’s noise.
Noise might be something like random static, but it could also simply be something you’re not interested in at the time. Maybe you’d like to pick out the bass line from a recording, and in that moment you’d consider the piano to be noise. No offense to the person on piano.
Or maybe you’re trying to determine the impact of an advertising campaign on sales. Your sales may go up and down for many reasons, but you’re trying to tease out the effect of one particular factor when there are other, maybe larger, factors in play. When two things go up and down together, time series analysis may be able to infer whether one causes the other or whether both depend on something else.
Maybe the noise itself is what you’re interested in because you want to control it. You might want to reduce it, or if that’s not possible, you might want to shift some of its spectrum to where it is less noticeable. Or rather than filter out noise, you might want to add noise into a system that doesn’t have enough noise because, counter-intuitively, noise can sometimes make a system more reliable.
|“John helped us solve a vexing and festering problem. Reducing it to mathematics and then to code required educated guesses, creative assumptions, intuition, deep knowledge of digital signal processing, and shots in the dark. This is where John excels: just the right mix of practical urgency with mathematical rigor. It’s difficult to overemphasize the difficulty of this problem and the acumen required to solve it completely and on a schedule. Just fantastic!” — Brian Beckman, PhD|
I can help you separate your signal from your noise by using powerful tools from digital signal processing and time series analysis. You can read more about my background here.
Gain more insight from your data and make more informed decisions.