Publications

@article{Gutierrez-Aviles2015,

title = {MSL: A Measure to Evaluate Three-dimensional Patterns in Gene Expression Data},

author = {D. Gutiérrez-Avilés and C. Rubio-Escudero},

url = {https://journals.sagepub.com/doi/10.4137/EBO.S25822},

doi = {10.4137/EBO.S25822},

year  = {2015},

date = {2015-01-01},

journal = {Evolutionary Bioinformatics},

volume = {11},

pages = {121—135},

abstract = {icroarray technology is highly used in biological research environments due to its ability to monitor the RNA concentration levels. The analysis of the data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior. Biclustering relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. Triclustering appears for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. These triclusters provide hidden information in the form of behavior patterns from temporal experiments with microarrays relating subsets of genes, experimental conditions, and time points. We present an evaluation measure for triclusters called Multi Slope Measure, based on the similarity among the angles of the slopes formed by each profile formed by the genes, conditions, and times of the tricluster.},

keywords = {bioinformatics, time series},

pubstate = {published},

tppubtype = {article}

}

@article{NEUCOM2015,

title = {A multi-scale smoothing kernel for measuring time-series similarity},

author = {A. Troncoso and M. Arias and J. C. Riquelme},

url = {https://www.sciencedirect.com/science/article/pii/S0925231215005585},

doi = {10.1016/j.neucom.2014.08.099},

year  = {2015},

date = {2015-01-01},

journal = {Neurocomputing},

volume = {167},

pages = {8-17},

abstract = {In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{RENE2015,

title = {Local models regression trees for very short-term wind speed predictions},

author = {A. Troncoso and S. Salcedo-Sanz and C. Casanova-Mateo and J. C. Riquelme and L. Prieto},

url = {https://www.sciencedirect.com/science/article/pii/S0960148115002530},

doi = {10.1016/j.renene.2015.03.071},

year  = {2015},

date = {2015-01-01},

journal = {Renewable Energy},

volume = {81},

pages = {589-598},

abstract = {This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem. We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{NEUCOM2015b,

title = {A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables},

author = {J. García-Gutierrez and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},

url = {https://www.sciencedirect.com/science/article/pii/S0925231215005524},

doi = {10.1016/j.neucom.2014.09.091},

year  = {2015},

date = {2015-01-01},

journal = {Neurocomputing},

volume = {167},

pages = {24-31},

abstract = {Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop those models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has emerged as a suitable tool to improve classic stepwise MLR results on LiDAR. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and regression techniques in machine learning (neural networks, support vector machines, nearest neighbour, ensembles such as random forests) with special emphasis on regression trees. The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results confirm that classic MLR is outperformed by machine learning techniques and concretely, our experiments suggest that Support Vector Regression with Gaussian kernels statistically outperforms the rest of the techniques.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2015,

title = {A Survey on Data Mining Techniques Applied To Electricity-Related Time Series Forecasting},

author = {F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés and J. C. Riquelme},

url = {https://www.mdpi.com/1996-1073/8/11/12361},

doi = {10.3390/en81112361},

year  = {2015},

date = {2015-01-01},

journal = {Energies},

volume = {8},

number = {11},

pages = {13162-13193},

abstract = {Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Gutierrez-Aviles2014,

title = {Mining 3D Patterns from Gene Expression Temporal Data: A New Tricluster Evaluation Measure},

author = {D. Gutiérrez-Avilés and C. Rubio-Escudero},

url = {http://www.hindawi.com/journals/tswj/2014/624371/},

doi = {10.1155/2014/624371},

year  = {2014},

date = {2014-01-01},

journal = {The Scientific World Journal},

volume = {2014},

pages = {1-16},

abstract = {Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project.},

keywords = {bioinformatics, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZALVAREZ2013198,

title = {Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula},

author = {F. Martínez-Álvarez and J. Reyes and A. Morales-Esteban and C. Rubio-Escudero},

url = {http://www.sciencedirect.com/science/article/pii/S0950705113001871},

doi = {10.1016/j.knosys.2013.06.011},

year  = {2013},

date = {2013-01-01},

journal = {Knowledge-Based Systems},

volume = {50},

pages = {198-210},

abstract = {This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the information gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been characterized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TKDE2011,

title = {Energy Time Series Forecasting Based on Pattern Sequence Similarity},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme and J. S. Aguilar-Ruiz},

url = {https://ieeexplore.ieee.org/document/5620917},

doi = {10.1109/TKDE.2010.227},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {23},

number = {8},

pages = {1230-1243},

abstract = {This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{PRL2011,

title = {Discovery of Motifs for Forecast Outlier Occurrence in Time Series},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme and J. S. Aguilar-Ruiz},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0167865511001371},

doi = {10.1016/j.patrec.2011.05.002},

year  = {2011},

date = {2011-01-01},

journal = {Pattern Recognition Letters},

number = {32},

pages = {1652–1665},

abstract = {The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{martinez2011,

title = {Clustering Preprocessing to Improve Time Series Forecasting},

author = {F. Martínez-Álvarez},

url = {http://dl.acm.org/citation.cfm?id=1937696.1937702},

year  = {2011},

date = {2011-01-01},

journal = {AI Commun.},

volume = {24},

number = {1},

pages = {97-98},

abstract = {This work proposes a novel general-purpose forecasting algorithm. It first extracts patterns from time series using the information provided by certain clustering techniques, which are applied as a first step of the approach. Moreover, the occurrence of data with especially unexpected values (outliers) is also addressed in this work. To deal with these outliers, a new hybrid methodology has been proposed, by inserting and adapting an existing approach based on the discovery of frequent episodes in sequences in the general scheme of prediction.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{IEEE2007,

title = {Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques},

author = {A. Troncoso and J. M. Riquelme-Santos and A. Gómez-Expósito and J. L. Martínez-Ramos and J. C. Riquelme},

url = {https://ieeexplore.ieee.org/document/4282040},

doi = {10.1109/TPWRS.2007.901670},

year  = {2007},

date = {2007-01-01},

journal = {IEEE Transactions on Power Systems},

volume = {22},

number = {3},

pages = {1294-1301},

abstract = {This paper presents a simple technique to forecast next-day electricity market prices based on the weighted nearest neighbors methodology. First, it is explained how the relevant parameters defining the adopted model are obtained. Such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction. Then, results corresponding to the Spanish electricity market during 2002 are presented and discussed. Finally, the performance of the proposed method is compared with that of recently published techniques.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{AICOM2005,

title = {Advances in Optimization and Prediction Techniques: Real-World Applications},

author = {A. Troncoso},

url = {https://content.iospress.com/articles/ai-communications/aic372},

year  = {2006},

date = {2006-01-01},

journal = {Artificial Intelligence Communications},

volume = {19},

number = {3},

pages = {295-297},

abstract = {This paper describes a time-series prediction method based on the k-Weighted Nearest Neighbours (k-WNN) algorithm and a simple technique to deal with nonconvex, nonlinear optimization problems by solving a sequence of Interior Point (IP) subproblems. The proposed prediction methodology is applied to obtain the 24-hour forecasts of two real time series: the demand and the energy prices in the competitive Spanish Electricity Market. The proposed optimization method is applied to the optimal scheduling of the electric energy production in the short-term.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}