In functional data analysis (FDA), data are considered as continuous functions (or curves). This is in contrast to multivariate statistics, where data are considered as vectors (finite sets of values).
Real data are usually collected as discrete samples. In FDA, such discrete data are transformed to a functional form through an interpolation process, e.g. using splines. In FDA, many statistical techniques developed in multivariate statistics for vector data are extended to functions.
FDA inherits many of its concepts from functional analysis, a branch of mathematical analysis. For example, a function 
is considered as a single point in a functional space 
. Functional spaces normally have an infinite number of dimensions, in contrast to vector spaces (like Euclidean space). The most famous functional space is “Hilbert space”.