关键词: 时间序列预测, 多任务学习, 最小二乘支持向量机, 相关性

Abstract: Considering the problems of insufficient information mining and low prediction accuracy in single task time series, a time series prediction method based on Multi Task LS-SVM（MTLS-SVM） is proposed. Multiple time series tasks are simultaneously studied so that task can be pinned down in the training process to induce inductive bias, which improves prediction accuracy. First of all, the several learning tasks are constructed by using the close correlation between adjacent time points. Then the MTLS-SVM model is trained for prediction by the corresponding data sets of each task. This method is applied to several time series data sets. Compared with the single task LS-SVM method, the experimental results show that the proposed method has high prediction accuracy and verify the feasibility and effectiveness.

Key words: time series prediction, multi-task learning, Least Squares Support Vector Machine（LS-SVM）, relativity