It's not just curve fitting because basis functions have characteristics which make them desirable for the kind of decomposition one is trying to find. We typically assume in factor analysis that factors are gaussian random variables without clear and repeating patterns. Fourrier transforms force us to think in similar terms but accounting for specific dynamics factor (I. E. Basis functions) should capture.
Also how do we construct those orthogonal basis functions for any downstream task is an interesting research question!
Location: Houston, TX
Remote: Yes
Willing to relocate: No but can travel.
Technologies: Python
Résumé/CV: linkedin.com/in/lionelyelibi
Email: yelibi@spincitylab.com
I am a research scientist currently working in financial services. My background is in physics, data science and machine learning. I have broad interests so hit me to talk about your project ideas or your firm's mission. Over the last +8yrs I have worked as IC or lead in various academic and industrial research projects. I'm looking for a real challenge and a group of like-minded folks to build with.
Also how do we construct those orthogonal basis functions for any downstream task is an interesting research question!