Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting

In multi-view regression, we have a regression problem where the input data can be represented in multiple ways. These different representations are called views. The aim of multi-view regression is to increase the performance of using only one view by taking into account the information available from all views. In this paper, we introduce a novel multi-view regression model called Multi-View Least Squares Support Vector Machines (MV LS-SVM) regression. This model is formulated in the primal-dual setting typical to Least Squares Support Vector Machines (LS-SVM) where a coupling term is introduced in the primal objective. This form of coupling allows for some degree of freedom to model the different representations while being able to incorporate the information from all views in the training phase. This work was motivated by the challenge of predicting temperature in weather forecasting. Black-box weather forecasting deals with a large number of observations and features and is one of the most challenging learning task around. In order to predict the temperature in a city, the historical data from that city as well as from the neighboring cities are taking into account. In the past, the data for different cities were usually simply concatenated. In this work, we use MV LS-SVM to do temperature prediction by regarding each city as a different view. Experimental results on the minimum and maximum temperature prediction in Brussels, show the improvement of the multi-view method with regard to previous work and that this technique is competitive to the existing state-of-the-art methods in weather prediction.