Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods.

DOI

10.2991/eusflat.2011.7How to use a DOI?

Keywords

Time series, Computational intelligence, Neural networks, Support vector machine, Fuzzy rules, Genetic algorithm

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.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Martin Stepnicka
AU  - Juan Peralta Donate
AU  - Paulo Cortez
AU  - Lenka Vavríková
AU  - German Gutierrez
PY  - 2011/08
DA  - 2011/08
TI  - Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods.
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 464
EP  - 471
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.7
DO  - 10.2991/eusflat.2011.7
ID  - Stepnicka2011/08
ER  -