Clustering-based feature selection for black-box weather temperature prediction

Reliable weather forecasting is one of the challenging tasks that deals with a large number of observations and features. In this paper, a data-driven modeling technique is proposed for temperature prediction. To investigate local learning, Soft Kernel Spectral Clustering (SKSC) is used to find similar samples to the test point to be used for training. Due to the high dimensionality, Elastic net is employed as a feature selection approach. Features are selected in each cluster independently and then, Least Squares Support Vector Machines (LS-SVM) regression is used to learn the data. Finally, the predicted values by LS-SVMs are averaged based on the membership of the test point to each cluster. In the experimental results, the performance of the proposed method and “Weather underground” are compared and it is shown that the data-driven technique is competitive with the existing weather temperature prediction sites. For the case study, the prediction of the temperature in Brussels is considered.