Online time series prediction based modified kernel recursive least-squares from random projection and adaptive update

Junzhu Ma, Min Han, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Kernel recursive least-squares (KRLS) has shown better predictive energy efficiency in time series prediction. However, in complex and non-stationary environment, there are still some problems of low prediction efficiency and accuracy. In view of these problems, we propose adaptive sparse KRLS (RP-ASKRLS) with random projection. RP-ASKRLS introduces random projection into KRLS, which can sparse data and maintain manifold information. On this basis, sliding window sparse strategy and adaptive update standard are integrated, which can effectively restrain the dimension of kernel matrix, and track time-varying characteristic. Therefore, RP-ASKRLS can not only availably constrain testing time, but also reduce computational complexity, thus better prediction effect is obtained. The experimental results show that RP-ASKRLS online prediction has better forecast performance.

Original languageEnglish
Title of host publication12th International Conference on Advanced Computational Intelligence, ICACI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages432-438
Number of pages7
ISBN (Electronic)9781728142487
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes
Event12th International Conference on Advanced Computational Intelligence, ICACI 2020 - Dali, Yunnan, China
Duration: 14 Aug 202016 Aug 2020

Publication series

Name12th International Conference on Advanced Computational Intelligence, ICACI 2020

Conference

Conference12th International Conference on Advanced Computational Intelligence, ICACI 2020
Country/TerritoryChina
CityDali, Yunnan
Period14/08/2016/08/20

Keywords

  • Adaptive sparse
  • Kernel recursive least squares
  • Random projection
  • Time series online prediction

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