![applied functional data analysis methods and case studies applied functional data analysis methods and case studies](https://media.nngroup.com/media/editor/2021/06/04/nng-ux-maturity-levels-38.png)
![applied functional data analysis methods and case studies applied functional data analysis methods and case studies](http://docplayer.nl/docs-images/53/14989802/images/page_3.jpg)
FDA provides the basis, a standard set of function families(BSplines, Fourier, Polynomial), that we can use as building blocks to construct almost any underlying function of interest. To transform discrete observations into a continuous curve, we fit a function. The method brings a new perspective to data treatment: 30 years of daily temperature now becomes 30 data samples, and so do your handwriting or 2D images of an object. But surprisingly, since more than two decades ago, Ramsay & Silverman have already written about a new research branch that defined time as a feature dimension, glued all the observations to a single entity and called it Functional Data Analysis (FDA).Īs in the name, functional data view data in the form of a function (or curve) y=f(t) that smoothly spreads across a continuum t, be it time, probability density, or even cyclical shape (draw it with a single stroke) of an object. There are indeed sophisticated algorithms, say, RNN, that can embed time recursively. However, despite being a solid factor indicating the shape pattern, to my knowledge, the time continuum primarily plays a non-essential role as a fixed frame capturing observation interdependency. But surprisingly, since more than two decades ago, Ramsay & Silverman have already Speaking of time-series data, people commonly relate to a sequence of observations taken at equal intervals across the time dimension. Speaking of time-series data, people commonly relate to a sequence of observations taken at equal intervals across the time dimension.