@conference {776,
	title = {Automatic Time Series Forecasting with GRNN: A Comparison with Other Models},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {198-209},
	abstract = {In this paper a methodology based on general regression neural networks for forecasting time series in an automatic way is presented. The methodology is aimed at achieving an efficient and fast tool so that a large amount of time series can be automatically predicted. In this sense, general regression neural networks present some interesting features, they have a fast single-pass learning and produce deterministic results. The methodology has been implemented in the R environment. A study of packages in R for automatic time series forecasting, including well-known statistical and computational intelligence models such as exponential smoothing, ARIMA or multilayer perceptron, is also done, together with an experimentation on running time and forecast accuracy based on data from the NN3 forecasting competition.},
	keywords = {Automatic forecasting, General regression neural networks, time series forecasting},
	doi = {https://doi.org/10.1007/978-3-030-20521-8_17},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@article {778,
	title = {Time Series Forecasting with KNN in R: the tsfknn Package},
	journal = {The R Journal},
	volume = {11},
	number = {2},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {12/2019},
	pages = {229-242},
	abstract = {In this paper the tsfknn package for time series forecasting using k-nearest neighbor regression is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.},
	doi = {https://doi.org/10.32614/RJ-2019-004},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and Francisco Charte and A.J. Rivera-Rivas}
}
@article {Mart{\'\i}nez2017,
	title = {A methodology for applying k-nearest neighbor to time series forecasting},
	journal = {Artificial Intelligence Review},
	year = {2017},
	month = {Nov},
	abstract = {In this paper a methodology for applying k-nearest neighbor regression on a time series forecasting context is developed. The goal is to devise an automatic tool, i.e., a tool that can work without human intervention; furthermore, the methodology should be effective and efficient, so that it can be applied to accurately forecast a great number of time series. In order to be incorporated into our methodology, several modeling and preprocessing techniques are analyzed and assessed using the N3 competition data set. One interesting feature of the proposed methodology is that it resolves the selection of important modeling parameters, such as k or the input variables, combining several models with different parameters. In spite of the simplicity of k-NN regression, our methodology seems to be quite effective.},
	issn = {1573-7462},
	doi = {10.1007/s10462-017-9593-z},
	url = {https://doi.org/10.1007/s10462-017-9593-z},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {655,
	title = {An ensemble method for time series forecasting with simple exponential smoothing},
	booktitle = {Conference Computational and Mathematical Methods in Science and Engineering},
	year = {2014},
	month = {07},
	address = {Rota, C{\'a}diz (Spain)},
	author = {M. J. del Jesus and Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@article {Rivera2011,
	title = {A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods},
	journal = {Applied Intelligence},
	volume = {34},
	number = {3},
	year = {2011},
	month = {Jun},
	pages = {331{\textendash}346},
	abstract = {Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established as the aim of this work. According to expert opinion, the use of exogenous variables and technical indicators can help in this task and must be included in the forecasting process. The amount of variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO2RBFN, a cooperative-competitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input variables and to the data sets with the selected set of input variables. The experimentation carried out shows that CO2RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input variables. Moreover, the feature selection methods applied to the data sets highlighted some influential variables which could be considered not only for the prediction but also for the description of the complex process involved in the medium-term forecasting of the olive oil price.},
	issn = {0924-669x},
	doi = {10.1007/s10489-011-0284-1},
	url = {https://doi.org/10.1007/s10489-011-0284-1},
	author = {A.J. Rivera-Rivas and P{\'e}rez-Recuerda, Pedro and M.D. P{\'e}rez-Godoy and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel}
}
@conference {10.1007/978-3-642-25274-7_27,
	title = {A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price},
	booktitle = {Advances in Artificial Intelligence},
	year = {2011},
	pages = {263{\textendash}272},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper we present a summary of the application of CO2RBFN, a evolutionary cooperative-competitive algorithm for Radial Basis Function Networks design, to the medium-term forecasting of the extra-virgen olive price, carry out by the SIMIDAT research group. The forecast is about the price at source of the extra-virgin olive oil six months ahead. The influential of the feature selection algorithms over the forecasting of the extra-virgin olive oil price has been analysed in this study and the results obtained with CO2RBFN have been compared with those obtained by different soft computing methods.},
	isbn = {978-3-642-25274-7},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and P{\'e}rez-Recuerda, Pedro and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Lozano, Jose A. and G{\'a}mez, Jos{\'e} A. and Moreno, Jos{\'e} A.}
}
@conference {10.1007/978-3-642-13022-9_21,
	title = {Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market},
	booktitle = {Trends in Applied Intelligent Systems},
	year = {2010},
	pages = {205{\textendash}214},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlighted. In this paper an adaptation of CO2RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO2RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.},
	isbn = {978-3-642-13022-9},
	author = {M.D. P{\'e}rez-Godoy and P{\'e}rez, Pedro and A.J. Rivera-Rivas and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Garc{\'\i}a-Pedrajas, Nicol{\'a}s and F. Herrera and Fyfe, Colin and Ben{\'\i}tez, Jos{\'e} Manuel and Ali, Moonis}
}
