The “fast fourier transform” (FFT) algorithm is a powerful tool for looking at time-based measurements in an interesting way, but do you understand what it does? This talk will start from basic geometry and explain what the fourier transform is, how to understand it, why it’s useful and show examples.
If you’re collecting time-series data (e.g. heart rate, stock prices, server usage, temperature) the fourier transform can be a useful tool for analyzing the underlying periodic nature of the data. But, what is it actually doing? In this talk we’ll start from the foundation of basic geometry and explain what the transform is doing. The talk will feature lots of animated graphics to take the mystery out of this powerful method … and to keep you from reading Twitter during the talk. We’ll look at example applications and example code on how to use it in practice, along with practical tips, like choosing the number of bins and what in the world “windowing” functions are.
Materials available here: https://github.com/gallamine/fft_oscon/