APPLICATION OF NON PARAMETRIC BASIS SPLINE (BSPLINE) IN TEMPERATURE FORECASTING

Abstract

Weather is important but hard to predictlay people and scientists alike will agree. The complexity of system limits the knowledge about it and therefore its predictability even over a few days. It is complex because many variables within the Earths
atmosphere, such as temperature and they do so nonlinearly. B-spline as a basis for one-dimensional regression and we extend this paper by using B-spline to construct a basis for bivariate regression. This construction gives a basis in two dimensions with local support and hence a fully flexible family of fitted mortality surfaces one of the principal motivations behind the use of B-spline as the basis of regression is that it does
not suffer from the lack of stability that can so bedevil ordinary polynomial regression. The essential difference is that B-spline have local non-zero support in contrast to the polynomial basis for standard regression. The optimal B-Spline models rely on the
optimal knots that has a minimum Generalized Cross Validation (GCV)

Keywords: Temperature, B-Spline, Generalized Cross Validation, non-parametric