@article {812,
	title = {Strategies for time series forecasting with generalized regression neural networks},
	journal = {Neurocomputing},
	volume = {491},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {509-521},
	doi = {https://doi.org/10.1016/j.neucom.2021.12.028},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and Mar{\'\i}a Pilar Fr{\'\i}as and Ana Mar{\'\i}a Mart{\'\i}nez-Rodr{\'\i}guez}
}
@article {773,
	title = {EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search},
	journal = {Integrated Computer-Aided Engineering},
	volume = {27},
	number = {3},
	year = {2020},
	month = {05/2020},
	pages = {211-231},
	abstract = {Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time. },
	keywords = {Autoencoder, evolutionary methods, Representation learning},
	doi = {https://doi.org/10.3233/ICA-200619},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@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}
}
@conference {771,
	title = {Automating Autoencoder Architecture Configuration: An Evolutionary Approach},
	booktitle = {International Work-Conference on the Interplay Between Natural and Artificial Computation},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {339-349},
	abstract = {Learning from existing data allows building models able to classify patterns, infer association rules, predict future values in time series and much more. Choosing the right features is a vital step of the learning process, specially while dealing with high-dimensional spaces. Autoencoders (AEs) have shown ability to conduct manifold learning, compressing the original feature space without losing useful information. However, there is no optimal AE architecture for all datasets. In this paper we show how to use evolutionary approaches to automate AE architecture configuration. First, a coding to embed the AE configuration in a chromosome is proposed. Then, two evolutionary alternatives are compared against exhaustive search. The results show the great superiority of the evolutionary way.},
	keywords = {Autoencoder, Deep learning, Evolutionary, Optimization},
	doi = {https://doi.org/10.1007/978-3-030-19591-5_35},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@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 {MARTINEZ201838,
	title = {Dealing with seasonality by narrowing the training set in time series forecasting with kNN},
	journal = {Expert Systems with Applications},
	volume = {103},
	year = {2018},
	pages = {38 - 48},
	abstract = {In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using kNN regression. The key idea is to forecast every different season using a different specialized kNN learner. Each learner is specialized because its training set only contains examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized kNN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to kNN, we think that other computational intelligence approaches could take advantage of it.},
	keywords = {NN regression, Seasonal time series, time series forecasting},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2018.03.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417418301441},
	author = {Francisco Mart{\'\i}nez and Mar{\'\i}a Pilar Fr{\'\i}as and M.D. P{\'e}rez-Godoy 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 {308,
	title = {A specialized lazy learner for time series forecasting},
	booktitle = {17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2017},
	year = {2017},
	note = {TIN2015-68854-R},
	month = {7},
	pages = {1397{\textendash}1403},
	address = {Costa Ballena, Rota, C{\'a}adiz (Spain)},
	abstract = {In a time series context the nearest neighbour algorithm looks for the historical observations most similar to the latest observations of the time series. However, some nearest neighbours can be misleading. In this paper we propose that, if prior information about the structure of the time series is known, the search space of possible neighbours can be narrowed so that some possibly misleading neighbours are avoided. This way a more effective forecasting method can be obtained.},
	isbn = {978-84-617-8694-7},
	author = {Francisco Mart{\'\i}nez and M.P. Fr{\'\i}as and Francisco Charte and A.J. Rivera-Rivas}
}
@article {article,
	title = {Detecting hidden periodicities for models with cyclical errors},
	journal = {Statistics and Its Interface},
	volume = {10},
	number = {1},
	year = {2017},
	month = {04},
	pages = {107-118},
	doi = {10.4310/SII.2017.v10.n1.a10},
	author = {Fr{\'\i}as, M.P. and Ivanov, Alexander and Leonenko, Nikolai and Francisco Mart{\'\i}nez and Dolores Ruiz-Medina, Mar{\'\i}a}
}
@conference {313,
	title = {Combining simple exponential smoothing models for time series forecasting},
	booktitle = {International work-conference on Time Series, ITISE 2016},
	year = {2016},
	note = {-},
	month = {6},
	pages = {635-644},
	address = {Granada (Spain)},
	abstract = {Simple exponentional smoothing is a well-known technique for forecasting univariate time series without trend and seasonality. Forecast combinations such as medians or means are known to improve the accuracy of point forecasts. In this paper we have experimented with combining the forecasts of several simple exponential smoothing models with different smoothing factors. Experimental results, using the M3-competition time series, show that the combined forecasts outperform the forecasts of the model that best fits the series.},
	author = {Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and Francisco Charte and M. J. del Jesus}
}
@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}
}
@conference {6706973,
	title = {A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments},
	booktitle = {The 2013 International Joint Conference on Neural Networks (IJCNN)},
	year = {2013},
	month = {Aug},
	pages = {1-8},
	abstract = {Many real applications are composed of data sets where the distribution of the classes is significantly different. These data sets are commonly known as imbalanced data sets. Proposed approaches that address this problem can be categorized into two types: data-based, which resample problem data in a preprocessing phase and algorithm-based which modify or create new methods to address the imbalance problem. In this paper, CO2 RBFN a cooperative-competitive design method for Radial Basis Function Networks that has previously demonstrated a good behaviour tackling imbalanced data sets, is tested using two different training weights algorithms, local and global, in order to gain knowledge about this problem. As conclusions we can outline that a more global optimizer training algorithm obtains worse results.},
	keywords = {Accuracy, Algorithm design and analysis, algorithm-based approach, CO2RBFN, cooperative-competitive design method for radial basis function networks, data-based approach, global optimization weights methods, global optimizer training algorithm, imbalanced data sets, learning (artificial intelligence), Least squares approximations, local optimization weights methods, Neurons, optimisation, radial basis function networks, Sociology, Training, training weights algorithms},
	issn = {2161-4407},
	doi = {10.1109/IJCNN.2013.6706973},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. del Jesus and Francisco Mart{\'\i}nez}
}
@inbook {677,
	title = {Introducci{\'o}n de informaci{\'o}n geogr{\'a}fica en terrenos 3D},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimensional},
	year = {2011},
	pages = {71-82},
	issn = {978-84-694-8374-9},
	author = {Aguilera-Garc{\'\i}a, {\'A}ngel and Jim{\'e}nez-Perez, Juan Roberto and Francisco Mart{\'\i}nez}
}
@inbook {678,
	title = {Operaciones Booleanas sobre pol{\'\i}gonos},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimiensional},
	year = {2011},
	pages = {101-110},
	publisher = {Edici{\'o}n propia},
	organization = {Edici{\'o}n propia},
	address = {Ja{\'e}n (Espa{\~n}a)},
	issn = {978-84-694-8374-9},
	author = {Francisco Mart{\'\i}nez and Aguilera-Garc{\'\i}a, {\'A}ngel and Jim{\'e}nez-Perez, Juan Roberto}
}
@inbook {676,
	title = {Visualizaci{\'o}n de medios participativos en entornos urbanos},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimiensional},
	year = {2011},
	pages = {151-157},
	edition = {Edici{\'o}n propia},
	issn = {978-84-694-8374-9},
	author = {Jim{\'e}nez-Perez, Juan Roberto and Francisco Mart{\'\i}nez and Aguilera-Garc{\'\i}a, {\'A}ngel}
}
@conference {Martinez2010,
	title = {Simulating multi-self-similar spatiotemporal models with CUDA},
	booktitle = {COMPSTAT 2010},
	year = {2010},
	pages = {341-341},
	address = {Par{\'\i}s, del 22 al 27 de Agosto},
	author = {Francisco Mart{\'\i}nez and Mar{\'\i}a Pilar Fr{\'\i}as-Bustamante and Mar{\'\i}a Dolores Ruiz-Medina}
}
@article {article,
	title = {The multi-LREP decomposition of solids and its application to a point-in-polyhedron inclusion test},
	journal = {The Visual Computer},
	volume = {26},
	year = {2010},
	month = {11},
	pages = {1361-1368},
	doi = {10.1007/s00371-009-0413-6},
	author = {Francisco Mart{\'\i}nez and Rueda, Antonio and Feito, Francisco}
}
@article {MARTINEZ20091177,
	title = {A new algorithm for computing Boolean operations on polygons},
	journal = {Computers \& Geosciences},
	volume = {35},
	number = {6},
	year = {2009},
	pages = {1177 - 1185},
	abstract = {This paper presents a new algorithm for computing Boolean operations on polygons. These kind of operations are frequently used in the geosciences in order to get spatial information from spatial data modeled as polygons. The presented algorithm is simple and easy to understand and implement. Let n be the total number of edges of all the polygons involved in a Boolean operation and k be the number of intersections of all the polygon edges. Our algorithm computes the Boolean operation in time O((n+k)log(n)). Finally, the proposed algorithm works with concave polygons with holes, and with regions composed of polygon sets. Furthermore, it can be easily adapted to work with self-intersecting polygons.},
	keywords = {Boolean operations polygons, Polygon clipping, Polygon overlay},
	issn = {0098-3004},
	doi = {https://doi.org/10.1016/j.cageo.2008.08.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0098300408002793},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda and Francisco Ram{\'o}n Feito}
}
@conference {Martinez2009,
	title = {Tests de inclusi{\'o}n punto en objeto basados en {\'a}rboles de intervalos},
	booktitle = {Congreso Espa{\~n}ol de Inform{\'a}tica Gr{\'a}fica},
	year = {2009},
	pages = {57-64},
	address = {San Sebasti{\'a}n},
	isbn = {978-3-905673-72-7},
	doi = {10.2312/LocalChapterEvents/CEIG/CEIG09/057-064},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz and Francisco Ram{\'o}n Feito-Higueruela}
}
@conference {Martnez2007ASA,
	title = {A simple and fast hardware-accelerated point-in-polygon test},
	booktitle = {GRAPP},
	year = {2007},
	author = {Francisco Mart{\'\i}nez and Antonio J. Rueda Ruiz and Francisco R. Feito-Higueruela}
}
@conference {Martinez2007,
	title = {Experimentando con la paralelizaci{\'o}n de un problema de c{\'a}lculo utilizando hardware gr{\'a}fico},
	booktitle = {XVIII Jornadas de Paralelismo},
	year = {2007},
	pages = {791-798},
	address = {Zaragoza, del 11 al 14 de Septiembre},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz and Francisco Ram{\'o}n Feito-Higueruela and Mar{\'\i}a Pilar Fr{\'\i}as-Bustamante}
}
@conference {Martinez2007,
	title = {La descomposici{\'o}n multi-L-REP de s{\'o}lidos y su aplicaci{\'o}n a un test de inclusi{\'o}n},
	booktitle = {XVII Congreso Espa{\~n}ol de Inform{\'a}tica Gr{\'a}fica},
	year = {2007},
	pages = {159-168},
	address = {Zaragoza, del 11 al 14 de Septiembre},
	isbn = {978-84-9732-595-0},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz}
}
@article {article,
	title = {The multi-L-REP decomposition and its application to a point-in-polygon inclusion test},
	journal = {Computers \& Graphics},
	volume = {30},
	year = {2006},
	month = {12},
	pages = {947-958},
	doi = {10.1016/j.cag.2006.08.015},
	author = {Francisco Mart{\'\i}nez and Rueda, Antonio and Feito, Francisco}
}
@conference {article,
	title = {Layer-based Decompositions of Polyhedra},
	booktitle = {International Conference in Central Europe on Computer Graphics Visualization and Computer Vision},
	year = {2005},
	month = {January},
	author = {Rueda, Antonio and Francisco Mart{\'\i}nez and Feito, Francisco}
}
@conference {726,
	title = {Acceso restringido a calificaciones de alumnos a trav{\'e}s de internet},
	booktitle = {Simposio Espa{\~n}ol de inform{\'a}tica distribuida},
	year = {2000},
	month = {09},
	address = {Orense (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and Jos{\'e} R. Balsas and Francisco Mart{\'\i}nez and Rivas, Victor M.}
}
