# DFT for timeseries data

### Jan 3 2018

DFT for Timeseries Data is a Jupyter Notebook I developed for a presentation on applying the DFT to timeseries data for seasonality detection. The notebook attempts to build understanding one step at a time from a direct mathematical implementation and explanation of the DFT, to implementing frequency magnitude detection and approximate resynthesis.

* Note: Being that this was designed to go along with a presentation, I would not recommend it as a first introduction to the DFT since it's really mostly code with some images for visual explanation.* Armed with some knowledge of the DFT already, I think it should help clarify some behaviors visually and could serve an implementation reference for some algorithms. I'm going to be digging into Julius O. Smith's

*Mathematics of the Discrete Fourier Transform*soon, so there should be more DFT-related notebooks to come!

Here are some of the animations built up in this notebook:

*Increasing the number of DFT points results in better reconstruction of the original series:**The effects of windowing a pure sinusoid when the window is not an exact multiple of the period:**The effect of window size on frequency magnitude accuracy:*