Time series online prediction algorithm based on least squares support vector machine

• 作 者: WU Qiong LIU Wen-ying YANG Yi-
• 机 构: Key Laboratory of Power System Protection and Dynamic Security Monitory and Control of Ministry of E
• 出 处: 中南工业大学学报
• 发表日期: 2004年期
• 关键词: time series prediction machine learning support vector machine statistical learn
• 基金项目: supported by the State Grid Cooperation of China

Deficiencies of applying the traditional least squares support vector machine (LS-SVM) to time series online prediction were specified. According to the kernel function matrixs property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to time series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM(75-1 600 ms), that of the proposed method in different time windows is 40-60 ms, and the prediction accuracy(normalized root mean squared error) of the proposed method is above 0.8. So the improved method is better than the traditional LS-SVM and more suitable for time series online prediction.