Functional Time Series Analysis of Land Surface Temperature

Abstract

Parametric modeling imposes rigid assumption on abstraction of physical characteristics of a phenomenon, which in case of model misspecification could give erroneous results. To address the drawbacks, efforts have been channeled on semi-parametric and nonparametric modeling and inference. This study focuses on constructing an estimator and consequently modeling a meteorological temperature time series first by constructing a penalized spline estimator based on cubic splines. The penalized spline estimator proposed, which are known to impose very minimal restrictions on estimation process, provides good fits to observed data with very attractive properties namely consistent as observed in values of the Mean Squared Error from the analysis. The results of our simulations compared favorably with the empirical analysis on average monthly meteorological temperature data obtained from Climate Knowledge Portal World Bank Organization on Ghana for periods 1901-2016.