K-NN based LS-SVM framework for long-term time series prediction

Zifang Huang, Mei Ling Shyu

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

16 Scopus citations

Abstract

Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function, which integrates the Euclidean distance and the dissimilarity of the trend of a time series, is defined for the k-NN approach. By selecting similar instances (i.e., nearest neighbors) in the training dataset for each testing instance based on the k-NN approach, the complexity of training an LS-SVM regressor is reduced significantly. Experiments on two types of datasets were conducted to compare the prediction performance of the proposed framework with the traditional LS-SVM approach and the LL-MIMO (Multi-Input Multi- Output Local Learning) approach at the prediction horizon 20. The experimental results demonstrate that the proposed framework outperforms both traditional LS-SVM approach and LL-MIMO approach in prediction. Furthermore, experimental results also show the promising long-term prediction ability of the proposed framework even when the prediction horizon is large (up to 180).

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Information Reuse and Integration, IRI 2010
Pages69-74
Number of pages6
DOIs
StatePublished - Oct 22 2010
Event11th IEEE International Conference on Information Reuse and Integration, IRI 2010 - Las Vegas, NV, United States
Duration: Aug 4 2010Aug 6 2010

Publication series

Name2010 IEEE International Conference on Information Reuse and Integration, IRI 2010

Other

Other11th IEEE International Conference on Information Reuse and Integration, IRI 2010
CountryUnited States
CityLas Vegas, NV
Period8/4/108/6/10

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

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    Huang, Z., & Shyu, M. L. (2010). K-NN based LS-SVM framework for long-term time series prediction. In 2010 IEEE International Conference on Information Reuse and Integration, IRI 2010 (pp. 69-74). [5558963] (2010 IEEE International Conference on Information Reuse and Integration, IRI 2010). https://doi.org/10.1109/IRI.2010.5558963