Forecasting seasonal time series with computational intelligence : contribution...

• Author(s): Stepnicka, M. ; Peralta Donate, Juan ; Cortez, Paulo, 1971- ; Vavricková, L. ; Gutierrez Sanchez, German
• Date: 2011
• Persistent ID: http://hdl.handle.net/1822/14840
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Time series; Computational intelligence; Neural networks; Support vector machine; Fuzzy rules; Genetic algorithm
• Document Type: Conference Object
• Language: English

Description

Accurate time series forecasting are important for displaying the manner in which the past contin- ues to affect the future and for planning our day to day activities. In recent years, a large litera- ture has evolved on the use of computational in- telligence in many forecasting applications. In this paper, several computational intelligence techniques (genetic algorithms, neural networks, support vec- tor machine, fuzzy rules) are combined in a distinct way to forecast a set of referenced time series. Fore- casting performance is compared to the a standard and method frequently used in practice.