Páginas 943-946
Cómo citar esta publicación
Pérez, P., Frías, M. P., Pérez Godoy, M. D., Rivera Rivas, A. J., Jesus, M. J. d., Parras, M., & Torres, F. J. (2008). An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market. 943-946. https://doi.org/10.1109/HIS.2008.132
Resumen
This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO2RBFN, for short-term forecasting of extra virgin olive oil price. The olive oil time series has been analyzed with a new evolutionary proposal for the design of RBFNs, CO2RBFN. Results obtained has been compared with ARIMA models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm and a radial basis function network trained with a LMS algorithm. The experimentation shows the high efficacy reached for the applied methods, specially for data mining methods which have slightly outperformed ARIMA methodology.
Palabras clave
- Algorithm design and analysis
- ARIMA
- ARIMA models
- Artificial neural networks
- autoregressive moving average processes
- CO2RBFN
- conjugate gradient algorithm
- conjugate gradient methods
- data mining
- data mining methods
- evolutionary cooperative competitive
- extra virgin olive oil price
- Forecasting
- fuzzy system
- Fuzzy systems
- GA-P algorithm
- genetic algorithms
- least mean squares methods
- LMS algorithm
- multilayer perceptron
- multilayer perceptrons
- Olive Oil Price
- olive oil time series
- Petroleum
- pricing
- radial basis function network
- radial basis function networks
- RBFN learning algorithm
- short-term forecasting
- Spanish market
- time series
- Time series analysis
- time series forecasting
- Training
- vegetable oils
Detalles
- Date Published
-
Sep.