Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:NEURAL COMPUTING & APPLICATIONS
Key Words:Time series prediction (TSP) Incremental SVM Incremental learning Double incremental learning (DIL) algorithm
Abstract:Based on support vector machine (SVM), incremental SVM was proposed, which has a strong ability to deal with various classification and regression problems. Incremental SVM and incremental learning paradigm are good at handling streaming data, and consequently, they are well suited for solving time series prediction (TSP) problems. In this paper, incremental learning paradigm is combined with incremental SVM, establishing a novel algorithm for TSP, which is the reason why the proposed algorithm is termed double incremental learning (DIL) algorithm. In DIL algorithm, incremental SVM is utilized as the base learner, while incremental learning is implemented by combining the existing base models with the ones generated on the new data. A novel weight update rule is proposed in DIL algorithm, being used to update the weights of the samples in each iteration. Furthermore, a classical method of integrating base models is employed in DIL. Benefited from the advantages of both incremental SVM and incremental learning, the DIL algorithm achieves desirable prediction effect for TSP. Experimental results on six benchmark TSP datasets verify that DIL possesses preferable predictive performance compared with other existing excellent algorithms.
ISSN No.:0941-0643
Translation or Not:no
Date of Publication:2019-10-01
Co-author:李锦华,叶锐
Correspondence Author:dq
Date of Publication:2019-10-01