Proposal to sliding window-based support vector regression

This paper proposes a new methodology, Sliding Window-based Support Vector Regression (SW-SVR), for micrometeorological data prediction. SVR is derived from a statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data. Although SVR is superior to traditional learning algorithms such as Artificial Neural Network (ANN), it is difficult to choose the suitable amount of training data to build an optimum SVR model for micrometeorological data prediction. This paper revealed the periodic characteristics of micrometeorological data and evaluated SW-SVR can adapt the appropriate amount of training data to build an optimum SVR model automatically using parallel distributed processing. The future prediction experiment was conducted on air temperature of Sapporo, Tokyo, Hamamatsu, and Naha. As a result, SW-SVR has improved prediction accuracy in Sapporo, and Tokyo. In addition, it has reduced calculation time by more than 96 % in all regions. (C) 2014 Published by Elsevier B.V.