Abstract
In This Paper, an Optimally Pruned Extreme Learning Machine (Op-Elm) is Applied to the Problem of Long-Term Time Series Prediction. Three Known Strategies for the Long-Term Time Series Prediction I.e. Recursive, Direct and Dirrec Are Considered in Combination with Op-Elm and Compared with a Baseline Linear Least Squares Model and Least-Squares Support Vector Machines (Ls-Svm). among These Three Strategies Dirrec is the Most Time Consuming and its Usage with Nonlinear Models Like Ls-Svm, Where Several Hyperparameters Need to Be Adjusted, Leads to Relatively Heavy Computations. It is Shown that Op-Elm, Being Also a Nonlinear Model, Allows Reasonable Computational Time for the Dirrec Strategy. in All Our Experiments, Except One, Op-Elm with Dirrec Strategy Outperforms the Linear Model with Any Strategy. in Contrast to the Proposed Algorithm, Ls-Svm Behaves Unstably Without Variable Selection. It is Also Shown that There is No Superior Strategy for Op-Elm: Any of Three Can Be the Best. in Addition, the Prediction Accuracy of an Ensemble of Op-Elm is Studied and It is Shown that Averaging Predictions of the Ensemble Can Improve the Accuracy (Mean Square Error) Dramatically. © 2013 Elsevier Ltd.
Recommended Citation
A. Grigorievskiy et al., "Long-Term Time Series Prediction using OP-ELM," Neural Networks, vol. 51, pp. 50 - 56, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.neunet.2013.12.002
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Direct strategy; DirRec strategy; ELM; LS-SVM; OP-ELM; Ordinary least squares; Recursive strategy; Time series prediction
International Standard Serial Number (ISSN)
1879-2782; 0893-6080
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2014
PubMed ID
24365536
