Multi-model integration for long-term time series prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Long-term (multi-step-ahead) time series prediction is a much more challenging task comparing to the short-term (one-step-ahead) time series prediction. This is due to the increasing uncertainty and the lack of knowledge about the future trend. In this paper, we propose a multi-model integration strategy to 1) generate predicted values using multiple predictive models; and then 2) integrate the predicted values to generate a final predicted value as the output. In the first step, a k-nearest-neighbor (k-NN) based least squares support vector machine (LS-SVM) approach is used for long-term time series prediction. An autoregressive model is then employed in the second step to combine the predicted values from the multiple k-NN based LS-SVM models. The proposed multi-model integration strategy is evaluated using six datasets, and the experimental results demonstrate that the proposed strategy consistently outperforms some existing predictors.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012
Pages116-123
Number of pages8
DOIs
StatePublished - Nov 8 2012
Event2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012 - Las Vegas, NV, United States
Duration: Aug 8 2012Aug 10 2012

Publication series

NameProceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012

Other

Other2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012
CountryUnited States
CityLas Vegas, NV
Period8/8/128/10/12

Keywords

  • autoregressive model
  • k-nearest-neighbor
  • least squares support vector machine (LS-SVM)
  • long-term time series prediction
  • multi-model integration

ASJC Scopus subject areas

  • Information Systems

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