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A hybrid method for tuning neural network for time series forecasting

Authors:

Aranildo Rodrigues Lima Junior,

Tiago Alessandro Espínola FerreiraAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 531 - 532

https://doi.org/10.1145/1389095.1389197

Published: 12 July 2008 Publication History

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Abstract

This paper presents an study about a new Hybrid method -GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatorial problems - Greedy Randomized Adaptive Search Procedure(GRASP) - and Evolutionary Strategies (ES) concepts. The GRAPES tuning and evolve the Artificial Neural Network parameters configuration, the weights and the minimum number of (and their specific) relevant time lags, searching an optimal or sub-optimal forecasting model for a correct time series representation. An experimental investigation is conducted with the GRASPES with some time series and the results achieved are discussed and compared, according to five well-known performance measures, to other works reported in the literature.

References

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Cited By

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Xie FSong ACiesielski V(2012)Learning time series patterns by genetic programmingProceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 12210.5555/2483654.2483661(57-62)Online publication date: 30-Jan-2012
https://dl.acm.org/doi/10.5555/2483654.2483661
Lima Junior ASilva DMattos Neto PFerreira TPelikan MBranke J(2010)An experimental study of fitness function and time series forecasting using artificial neural networksProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830848(2015-2018)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830848
de M. Neto PPetry GRodrigues LFerreira T(2009)Combining artificial neural network and particle swarm system for time series forecastingProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704619(2417-2424)Online publication date: 14-Jun-2009
https://dl.acm.org/doi/10.5555/1704555.1704619

Index Terms

A hybrid method for tuning neural network for time series forecasting
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Xie FSong ACiesielski V(2012)Learning time series patterns by genetic programmingProceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 12210.5555/2483654.2483661(57-62)Online publication date: 30-Jan-2012
https://dl.acm.org/doi/10.5555/2483654.2483661
Lima Junior ASilva DMattos Neto PFerreira TPelikan MBranke J(2010)An experimental study of fitness function and time series forecasting using artificial neural networksProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830848(2015-2018)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830848
de M. Neto PPetry GRodrigues LFerreira T(2009)Combining artificial neural network and particle swarm system for time series forecastingProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704619(2417-2424)Online publication date: 14-Jun-2009
https://dl.acm.org/doi/10.5555/1704555.1704619

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A hybrid method for tuning neural network for financial time series forecasting

A comparison of neural network methods and Box-Jenkins model in time series analysis

Evolving time-lagged feedforward neural networks for time series forecasting

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