Synergistic Approach for Combining SVM Algorithms for Wind Speed Prediction

This work presents synergistic approach for combining Directed Acyclic Graph (DAG), Binary Tree (BT) and Binary Decision Tree (BDT) based Support Vector Machine (SVM) algorithms for predicting wind speed. The proposed approach and individual algorithms are evaluated on wind speed data that has many samples divided into training and test data sets. The proposed approach of using the synergistic approach produces better results than Directed Acyclic Graph, Binary Tree, and Binary Decision Tree (BDT) based multiclass SVM algorithms individually.