Short-term prediction of land surface temperature using multifractal detrended fluctuation analysis

Nature changes herself continuously at every moment. Hence, the prediction of any natural phenomenon (like weather), with hundred percent accuracy, is an extremely challenging task. But in spite of all uncertainties, there exists a rhythm and intrinsic regularity in all natural events. This paper presents a novel approach to predict the land surface temperature (LST) of a particular region using the theory of fractals. Although there exist several approaches for temperature prediction, there is only little work that captures the past regularities in the system dynamics while doing the prediction. In this paper we have described a prediction framework which at first captures the regularities in the dynamics of the LST series by estimating its generalized multifractal dimensions using Multifractal Detrended Fluctuation Analysis (MF-DFA). Then the prediction is performed on the basis of these captured regularities in system dynamics. The proposed approach has been evaluated with the LST data sets (of 60 years) collected from FetchClimate Explore of Microsoft Research. The results show that the proposed approach predicts the LST more accurately than several other existing prediction techniques.