计算机工程与应用2018，Vol.54Issue(3)：233-237,5.DOI:10.3778/j.issn.1002-8331.1608-0423
多任务LS-SVM在时间序列预测中的应用
Multi-task LS-SVM for application of time series prediction
摘要
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.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Key words
time series prediction/multi-task learning/Least Squares Support Vector Machine(LS-SVM)/relativity分类
信息技术与安全科学引用本文复制引用
贾松达,庞宇松,阎高伟..多任务LS-SVM在时间序列预测中的应用[J].计算机工程与应用,2018,54(3):233-237,5.基金项目
国家自然科学基金(No.61450011) （No.61450011）
山西省自然科学基金(No.2015011052). （No.2015011052）
