title: |
Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods. |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 464 - 471 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
M. Stepnicka, J. Peralta, P. Cortez, L. Vavr¨ªckov¨¢, G. Gutierrez |
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publication date: |
July 2011 |
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keywords: |
Time series, Computational intelligence, Neural networks, Support vector machine,
Fuzzy rules, Genetic algorithm |
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abstract: |
Accurate time series forecasting are important for
displaying the manner in which the past continues to affect the future and for planning our day
to day activities. In recent years, a large literature has evolved on the use of computational intelligence in many forecasting applications. In this
paper, several computational intelligence techniques
(genetic algorithms, neural networks, support vector machine, fuzzy rules) are combined in a distinct
way to forecast a set of referenced time series. Forecasting performance is compared to the a standard
and method frequently used in practice. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |