At their most basic level Fourier series are commonly used for periodic and near periodic data (such as for weather data and some economic data), whilst spline-based functions are used for non-periodic data \cite{Ramsay_2009}. Beyond these higher level distinctions, polynomial, B-spline (which are essentially built up of many polynomial sections), and wavelet functions can also be considered, with B-splines found to be better suited to fitting highly curvy data (where polynomials would require a large number of basis functions to achieve the same degree of fit - as such, splines have largely replaced polynomials now). An illustration of how a typical B-spline basis system might appear (in this case with 54 basis functions) is shown in Fig. \ref{746074}, whilst an example of how curve fits are achieved through the scaling of individual basis functions within such a basis system is shown in Fig. \ref{728398}.