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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

17-03-2018 | Original Article

A novel double incremental learning algorithm for time series prediction

Authors: Jinhua Li, Qun Dai, Rui Ye

Published in: Neural Computing and Applications | Issue 10/2019

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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.
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Metadata
Title
A novel double incremental learning algorithm for time series prediction
Authors
Jinhua Li
Qun Dai
Rui Ye
Publication date
17-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3434-0

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