Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods.
- Martin Stepnicka, Juan Peralta Donate, Paulo Cortez, Lenka Vavríková, German Gutierrez
- Corresponding Author
- Martin Stepnicka
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.7How to use a DOI?
- Time series, Computational intelligence, Neural networks, Support vector machine, Fuzzy rules, Genetic algorithm
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 464 EP - 471 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.7 DO - https://doi.org/10.2991/eusflat.2011.7 ID - Stepnicka2011/08 ER -