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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Long-term time series prediction using OP-ELM.

Alexander Grigorievskiy1, Yoan Miche1, Anne-Mari Ventelä2

  • 1Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland.

Neural Networks : the Official Journal of the International Neural Network Society
|December 25, 2013
PubMed
Summary
This summary is machine-generated.

Optimally Pruned Extreme Learning Machine (OP-ELM) effectively predicts long-term time series. Combining OP-ELM with the DirRec strategy offers computational efficiency and superior performance over linear models.

Keywords:
DirRec strategyDirect strategyELMLS-SVMOP-ELMOrdinary least squaresRecursive strategyTime series prediction

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Area of Science:

  • Machine Learning
  • Time Series Analysis
  • Computational Intelligence

Background:

  • Long-term time series prediction is crucial for various applications.
  • Existing methods like Least-Squares Support Vector Machines (LS-SVM) can be computationally intensive.
  • Optimally Pruned Extreme Learning Machine (OP-ELM) offers a potential alternative.

Purpose of the Study:

  • To evaluate the performance of OP-ELM for long-term time series prediction.
  • To compare OP-ELM with baseline models and different prediction strategies.
  • To investigate the impact of ensemble methods on prediction accuracy.

Main Methods:

  • Application of Optimally Pruned Extreme Learning Machine (OP-ELM).
  • Comparison of Recursive, Direct, and DirRec prediction strategies.
  • Benchmarking against linear least squares and Least-Squares Support Vector Machines (LS-SVM).

Main Results:

  • OP-ELM with the DirRec strategy demonstrated computational feasibility and outperformed linear models.
  • OP-ELM showed stable performance, unlike LS-SVM without variable selection.
  • No single prediction strategy was universally superior for OP-ELM.
  • Ensemble methods significantly improved prediction accuracy (Mean Square Error).

Conclusions:

  • OP-ELM is a viable and computationally efficient tool for long-term time series prediction.
  • The choice of prediction strategy can be flexible with OP-ELM.
  • Ensemble learning further enhances the predictive power of OP-ELM.