Automatic identification of ARIMA time series by expert systems using paradigms of artificial intelligence
- Valenzuela, O.
- Márquez, L.
- Pasadas Fernández, Miguel
- Rojas Ruiz, Ignacio
- Palacios Latasa, Manuel Pedro (coord.)
- Trujillo, David (coord.)
- Torrens Iñigo, Juan José (coord.)
- Madaune-Tort, Monique (coord.)
- López de Silanes Busto, María Cruz (coord.)
- Sanz Sáiz, Gerardo (coord.)
Editorial: Prensas de la Universidad de Zaragoza ; Universidad de Zaragoza
ISBN: 84-7733-720-9
Año de publicación: 2003
Páginas: 425-435
Congreso: Jornadas Zaragoza-Pau de Matemática Aplicada y Estadística (8. 2003. Jaca)
Tipo: Aportación congreso
Resumen
In this study we seek to resolve one of the most important problems in time series, the identification of the model, using the Box-Jenkins method. Our goal is to obtain an expert system based on paradigms of artificial intelligence, such as fuzzy logic and genetic algorithms, so that the model can be identified automatically, without the necessity for a human expert to intervene. A set of rules based on fuzzy logic is constructed, using as the main source of information the evolution and behaviour of the coefficients of autocorrelation and partial autocorrelation obtained from the time series. Each rule of the expert system is assigned a weight that determines the importance of this rule in the phase of model identification. A priori, the relevance of the rules is unknown, and so the rule system constructed is optimised by means of genetic algorithms.