Feature Extraction for Time Series, from Theory to Practice, with Python | by Piero Paialunga

Time series are a special animal.

When I started my Machine Learning career I did it because I loved Physics (weird reason to start Machine Learning) and from Physics I understood that I also loved coding and data science a lot. I didn’t really care about the type of data. All I wanted was to be in front of a computer writing 10k lines of code per day.

The truth is that even when you don’t care (I still really don’t) your career will drift you to some kinds of data rather than others.

If you work at SpaceX, you probably won’t do a lot of NLP but you will do a lot of signal processing. If you work at Netflix, you might end up working with a lot of NLP and recommendation systems. If you work at Tesla you will most definitely be a Computer Vision expert and work with images.

When I started as a Physicist, and then I kept going with my PhD in Engineering, I was immediately thrown into the world of signals.
This is just the natural world of engineering: every time you have a setup and extract the information from it, at the end of the day, you treat a signal. Don’t get me wrong…