Publications

@article{PEREZ20,

title = {Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand},

author = {R. Pérez-Chacón and G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025520306010},

doi = {10.1016/j.ins.2020.06.014},

year  = {2020},

date = {2020-06-06},

journal = {Information Sciences},

volume = {540},

pages = {160-174},

abstract = {This work proposes a novel algorithm to forecast big data time series. Based on the well-established Pattern Sequence Forecasting algorithm, this new approach has two major contributions to the literature. First, the improvement of the aforementioned algorithm with respect to the accuracy of predictions, and second, its transformation into the big data context, having reached meaningful results in terms of 

scalability. The algorithm uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust. Physical and cloud clusters have been used to carry out the experimentation, which consisted in applying the algorithm to real-world data from Uruguay electricity demand.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{NAZERIYE20,

title = {Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption - A Data Mining Approach},

author = {M. Nazeriye and A. Haeri and F. Martínez-Álvarez},

url = {https://www.mdpi.com/2076-3417/10/10/3589/},

doi = {https://doi.org/10.3390/app10103589},

year  = {2020},

date = {2020-05-22},

journal = {Applied Sciences},

volume = {10},

number = {10},

pages = {3589},

abstract = {Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{FERNANDEZ20,

title = {Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration},

author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S2214579620300034},

doi = {10.1016/j.bdr.2020.100135},

year  = {2020},

date = {2020-05-12},

journal = {Big Data Research},

volume = {19-20},

pages = {100135},

abstract = {The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment and big data analytics. However, to get started up is still a task that may take much time when manually done, due to the requisites that all nodes must fulfill. This work introduces LadonSpark, an open-source and non-commercial solution to configure and deploy a Spark cluster automatically. It has been specially designed for easy and efficient management of a Spark cluster with a friendly graphical user interface to automate the deployment of a cluster and to start up the distributed file system of Hadoop quickly. Moreover, LadonSpark includes the functionality of integrating any algorithm into the system. That is, the user only needs to provide the executable file and the number of required inputs for proper parametrization. Source codes developed in Scala, R, Python, or Java can be supported on LadonSpark. Besides, clustering, regression, classification, and association rules algorithms are already integrated so that users can test its usability from its initial installation.},

keywords = {big data, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ASIM20d,

title = {Seismicity Analysis and Machine Learning Models for Short-Term Low Magnitude},

author = {K. Asim and E Elawadi and F. Martínez-Álvarez and I. A. Niaz and S. R. M. Sayed and T. Iqbal},

url = {https://www.sciencedirect.com/science/article/pii/S0267726119302192},

doi = {https://doi.org/10.1016/j.soildyn.2019.105932},

year  = {2020},

date = {2020-03-01},

journal = {Soil Dynamics and Earthquake Engineering},

volume = {130},

pages = {id105932},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{BUI20,

title = {A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area},

author = {D. T. Bui and N.-D. Hoang and F. Martínez-Álvarez and P.-T. T. Ngo and P. V. Hoa and T. D. Pham and P. Samui and R. Costache},

url = {https://www.sciencedirect.com/science/article/pii/S0048969719344043},

doi = {https://doi.org/10.1016/j.scitotenv.2019.134413},

year  = {2020},

date = {2020-01-20},

journal = {Science of the Total Environment},

volume = {701},

pages = {id134413},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{GUIJO20,

title = {Time series clustering based on segment typologies extraction},

author = {D. Guijo-Rubio and A. M. Durán-Rosal and P. A. Gutiérrez and A. Troncoso and C. Hervás-Martínez},

doi = {10.1109/TCYB.2019.2962584},

year  = {2020},

date = {2020-01-15},

journal = {IEEE Transactions on Cybernetics},

abstract = {Time-series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time-series objects of the dataset. In this article, we propose a novel technique of time-series clustering consisting of two clustering stages. In a first step, a least-squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all of the segments are projected into the same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time-series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against three state-of-the-art methods, showing that the performance of this methodology is very promising, especially on larger datasets.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{TRULL20a,

title = {Initialization methods for multiple seasonal Holt–Winters forecasting models},

author = {Ó. Trull and J.C. García-Díaz and A. Troncoso},

doi = {10.3390/math8020268 },

year  = {2020},

date = {2020-01-01},

journal = {Mathematics},

volume = {8},

number = {2},

pages = {268},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Trull20b,

title = {Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain},

author = {Óscar Trull and J. Carlos García-Díaz and A. Troncoso},

doi = {10.3390/app10072630},

year  = {2020},

date = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {7},

pages = {2630},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{GOMEZ19,

title = {A novel ensemble method for electric vehicle power consumption forecasting: application to the Spanish system},

author = {C. Gómez-Quiles and G. Asencio-Cortés and A. Gastalver-Rubio and F. Martínez-Álvarez and A. Troncoso and J. Manresa and J. C. Riquelme and 

J. M. Riquelme},

url = {https://ieeexplore.ieee.org/document/8807120},

doi = {https://doi.org/10.1109/ACCESS.2019.2936478},

year  = {2019},

date = {2019-08-01},

journal = {IEEE Access},

volume = {7},

pages = {120840-120856},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ19,

title = {Big data and natural disasters: New approaches for temporal and spatial massive data analysis (Editorial)},

author = {F. Martínez-Álvarez and A. Morales-Esteban},

url = {https://www.sciencedirect.com/science/article/pii/S009830041930411X?dgcid=rss_sd_all},

doi = {https://doi.org/10.1016/j.cageo.2019.04.012},

year  = {2019},

date = {2019-08-01},

journal = {Computers and Geosciences},

volume = {129},

pages = {38-39},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TORRES19-1,

title = {Big data solar power forecasting based on deep learning and multiple data sources},

author = {J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12394},

doi = {https://doi.org/10.1111/exsy.12394},

year  = {2019},

date = {2019-03-01},

journal = {Expert Systems},

volume = {36},

pages = {id12394},

keywords = {deep learning, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{NEUCOM2019,

title = {MV-kWNN: A novel multivariate and multi-output weighted nearest neighbors algorithm for big data time series forecasting},

author = {R. Talavera-Llames and R. Pérez-Chacón and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0925231219303236?via%3Dihub},

doi = {10.1016/j.neucom.2018.07.092},

year  = {2019},

date = {2019-01-01},

journal = {Neurocomputing},

volume = {353},

pages = {56-73},

abstract = {This paper introduces a novel algorithm for big data time series forecasting. Its main novelty lies in its ability to deal with multivariate data, i.e. to consider multiple time series simultaneously, in order to make multi-output predictions. Real-world processes are typically characterised by several interrelated variables, and the future occurrence of certain time series cannot be explained without understanding the influence that other time series might have on the target time series. One key issue in the context of the multivariate analysis is to determine a priori whether exogenous variables must be included in the model or not. To deal with this, a correlation analysis is used to find a minimum correlation threshold that an exogenous time series must exhibit, in order to be beneficial. Furthermore, the proposed approach has been specifically designed to be used in the context of big data, thus making it possible to efficiently process very large time series. To evaluate the performance of the proposed approach we use data from Spanish electricity prices. Results have been compared to other multivariate approaches showing remarkable improvements both in terms of accuracy and execution time.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{en12010094b,

title = {A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand},

author = {F. Martinez-Alvarez and A. Schmutz and G. Asencio-Cortes and J. Jacques},

url = {http://www.mdpi.com/1996-1073/12/1/94},

doi = {10.3390/en12010094},

issn = {1996-1073},

year  = {2019},

date = {2019-01-01},

journal = {Energies},

volume = {12},

number = {94},

pages = {1-18},

abstract = {The forecasting of future values is a very challenging task. In almost all scientific disciplines, the analysis of time series provides useful information and even economic benefits. In this context, this paper proposes a novel hybrid algorithm to forecast functional time series with arbitrary prediction horizons. It integrates a well-known clustering functional data algorithm into a forecasting strategy based on pattern sequence similarity, which was originally developed for discrete time series. The new approach assumes that some patterns are repeated over time, and it attempts to discover them and evaluate their immediate future. Hence, the algorithm first applies a clustering functional time series algorithm, i.e., it assigns labels to every data unit (it may represent either one hour, or one day, or any arbitrary length). As a result, the time series is transformed into a sequence of labels. Later, it retrieves the sequence of labels occurring just after the sample that we want to be forecasted. This sequence is searched for within the historical data, and every time it is found, the sample immediately after is stored. Once the searching process is terminated, the output is generated by weighting all stored data. The performance of the approach has been tested on real-world datasets related to electricity demand and compared to other existing methods, reporting very promising results. Finally, a statistical significance test has been carried out to confirm the suitability of the election of the compared methods. In conclusion, a novel algorithm to forecast functional time series is proposed with very satisfactory results when assessed in the context of electricity demand.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2019,

title = {Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter},

author = {Ó. Trull and J. C. García-Díaz and A. Troncoso },

url = {https://www.mdpi.com/1996-1073/12/6/1083},

doi = {10.3390/en12061083},

year  = {2019},

date = {2019-01-01},

journal = {Energies},

volume = {12},

number = {6},

pages = {1083},

abstract = {Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TEHRANY19,

title = {A Novel Ensemble Modelling Approach for the Spatial Prediction of Tropical Forest Fire Susceptibility Using Logitboost Machine Learning Classifier and Multi-source Geospatial Data},

author = {M. S. Tehrany and S. Jones and F. Shabani and F. Martínez-Álvarez and D. T. Bui},

url = {https://link.springer.com/article/10.1007/s00704-018-2628-9},

doi = {https://doi.org/10.1007/s00704-018-2628-9},

year  = {2019},

date = {2019-01-01},

journal = {Theoretical and Applied Climatology},

volume = {137},

pages = {637-653},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{GALICIA19,

title = {Multi-step forecasting for big data time series based on ensemble learning},

author = {A. Galicia and R. Talavera-Llames and A. Troncoso and I. Koprinska and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0950705118304957},

doi = {https://doi.org/10.1016/j.knosys.2018.10.009},

year  = {2019},

date = {2019-01-01},

journal = {Knowledge Based-Systems},

volume = {163},

pages = {830-841},

keywords = {big data, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2019b,

title = {A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings},

author = {F. Divina and M. García-Torres and F. Goméz-Vela and J.L. Vázquez Noguera},

url = {https://www.mdpi.com/1996-1073/12/10/1934},

doi = {https://doi.org/10.3390/en12101934},

year  = {2019},

date = {2019-01-01},

journal = {Applied Sciences},

volume = {12},

number = {10},

pages = {1934},

abstract = {Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Gutierrez-Aviles2018,

title = {TRIQ: a new method to evaluate triclusters},

author = {D. Gutiérrez-Avilés and R. Giráldez and F. J. Gil-Cumbreras and C. Rubio-Escudero},

url = {https://biodatamining.biomedcentral.com/articles/10.1186/s13040-018-0177-5},

doi = {10.1186/s13040-018-0177-5},

year  = {2018},

date = {2018-01-01},

journal = {BioData Mining},

volume = {11},

number = {1},

pages = {15},

abstract = {Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO).},

keywords = {bioinformatics, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ENV2018,

title = {Imbalanced classification techniques for monsoon forecasting based on a new climatic time series},

author = {A. Troncoso and P. Ribera and G. Asencio-Cortés and I. Vega and D. Gallego},

url = {https://www.sciencedirect.com/science/article/pii/S1364815217301950},

doi = {10.1016/j.envsoft.2017.11.024},

year  = {2018},

date = {2018-01-01},

journal = {Environmental Modelling & Software},

volume = {106},

number = {6},

pages = {48-56},

abstract = {Monsoons have been widely studied in the literature due to their climatic impact related to precipitation 

and temperature over different regions around the world. In this work, data mining techniques, namely 

imbalanced classification techniques, are proposed in order to check the capability of climate indices to 

capture and forecast the evolution of theWestern North Pacific Summer Monsoon. Thus, the main goal is 

to predict if the monsoon will be an extreme monsoon for a temporal horizon of a month. Firstly, a new 

monthly index of the monsoon related to its intensity has been generated. Later, the problem of forecasting 

has been transformed into a binary imbalanced classification problem and a set of representative 

techniques, such as models based on trees, models based on rules, black box models and ensemble 

techniques, are applied to obtain the forecasts. From the results obtained, it can be concluded that the 

methodology proposed here reports promising results according to the quality measures evaluated and 

predicts extreme monsoons for a temporal horizon of a month with a high accuracy.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2018,

title = {Big data analytics for discovering electricity consumption patterns in smart cities},

author = {R. Pérez-Chacón and J. M. Luna and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},

url = {http://www.mdpi.com/1996-1073/11/3/683

},

doi = {10.3390/en11030683 },

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {3},

pages = {683},

abstract = {New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{INFSCI2018,

title = {A novel Spark-based multi-step forecasting algorithm for big data time series},

author = {A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025518304493},

doi = {10.1016/j.ins.2018.06.010},

year  = {2018},

date = {2018-01-01},

journal = {Information Sciences},

volume = {467},

pages = {800-818},

abstract = {This paper presents different scalable methods for predicting big time series, namely time series with a high frequency measurement. Methods are also developed to deal with arbitrary prediction horizons. The Apache Spark framework is proposed for distributed computing in order to achieve the scalability of the methods. Prediction methods have been developed using Spark’s MLlib library for machine learning. Since the library does not support multivariate regression, the prediction problem is formulated as h prediction sub-problems, where h is the number of future values to predict, that is, the prediction horizon. Furthermore, different kinds of representative methods have been chosen, such as decision trees, two tree-based ensemble techniques (Gradient-Boosted and Random Forest) and a linear regression method as a reference method for comparisons. Finally, the methodology has been tested in a real time series of electrical demand in Spain, with a time interval of ten minutes between measurements.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{KNOSYS2018b,

title = {Big data time series forecasting based on nearest neighbors distributed computing with Spark},

author = {R. Talavera-Llames and R. Pérez-Chacón and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0950705118303770},

doi = {10.1016/j.knosys.2018.07.026},

year  = {2018},

date = {2018-01-01},

journal = {Knowledge-Based Systems},

volume = {161},

number = {1},

pages = {12-25},

abstract = {A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm is introduced in this work. Such an algorithm has been developed for distributed computing under the Apache Spark framework. Every phase of the algorithm is explained in this work, along with how the optimal values of the input parameters required for the algorithm are obtained. In order to test the developed algorithm, a Spanish energy consumption big data time series has been used. The accuracy of the prediction has been assessed showing remarkable results. Additionally, the optimal configuration of a Spark cluster has been discussed. Finally, a scalability analysis of the algorithm has been conducted leading to the conclusion that the proposed algorithm is highly suitable for big data environments.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ICAE2018,

title = {A scalable approach based on deep learning for big data time series forecasting},

author = {J. F. Torres and A. Galicia and A. Troncoso and F. Martínez-Álvarez},

url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica580},

doi = {10.3233/ICA-180580},

year  = {2018},

date = {2018-01-01},

journal = {Integrated Computer-Aided Engineering},

volume = {25},

number = {4},

pages = {335-348},

abstract = {This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distributed computing. Since H2O does not allow the conduction of multi-step regression, a general-purpose methodology that can be used for prediction horizons with arbitrary length is proposed here, being the prediction horizon, h, the number of future values to be predicted. The solution consists in splitting the problem into h forecasting subproblems, being h the number of samples to be simultaneously predicted. Thus, the best prediction model for each subproblem can be obtained, making easier its parallelization and adaptation to the big data context. Moreover, a grid search is carried out to obtain the optimal hyperparameters of the deep learning-based approach. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 are reported. In particular, the accuracy and runtimes versus computing resources and size of the dataset are analyzed. Finally, the performance and the scalability of the proposed method is compared to other recently published techniques, showing to be a suitable method to process big data time series.},

keywords = {deep learning, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Florido2018,

title = {A novel tree-based algorithm to discover seismic patterns in earthquake catalogs},

author = {E. Florido and G. Asencio-Cortes and J. L. Aznarte and C. Rubio-Escudero and F. Martinez-Alvarez},

doi = {10.1016/j.cageo.2018.03.005},

issn = {0098-3004},

year  = {2018},

date = {2018-01-01},

journal = {Computers and Geosciences},

number = {115},

pages = {96-104},

abstract = {A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Shang2018b,

title = {Data field-based K-means clustering for spatio-temporal seismicity analysis and hazard assessment},

author = {X. Shang and X. Li and A. Morales-Esteban and G. Asencio-Cortes and Z. Wang},

doi = {10.3390/rs10030461},

issn = {2072-4292},

year  = {2018},

date = {2018-01-01},

journal = {Remote Sensing},

volume = {10},

number = {461},

pages = {1-22},

abstract = {Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts' seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Asencio-Cortes2018b,

title = {Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure},

author = {G. Asencio-Cortes and A. Morales-Esteban and X. Shang and F. Martinez-Alvarez},

doi = {10.1016/j.cageo.2017.10.011},

issn = {0098-3004},

year  = {2018},

date = {2018-01-01},

journal = {Computers and Geosciences},

number = {115},

pages = {198-210},

abstract = {Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Gomez-Losada2018b,

title = {A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information},

author = {A. Gomez-Losada and G. Asencio-Cortes and F. Martinez-Alvarez and J. C. Riquelme},

doi = {10.1016/j.envsoft.2018.08.013},

issn = {1364-8152},

year  = {2018},

date = {2018-01-01},

journal = {Environmental Modelling and Software},

number = {110},

pages = {52-61},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{BOKDE201888,

title = {A novel imputation methodology for time series based on pattern sequence forecasting},

author = {N. Bokde and Marcus W. Beck and F. Martínez-Álvarez and K. Kulat},

url = {http://www.sciencedirect.com/science/article/pii/S0167865518306500},

doi = {10.1016/j.patrec.2018.09.020},

year  = {2018},

date = {2018-01-01},

journal = {Pattern Recognition Letters},

volume = {116},

pages = {88-96},

abstract = {The Pattern Sequence Forecasting (PSF) algorithm is a previously described algorithm that identifies patterns in time series data and forecasts values using periodic characteristics of the observations. A new method for univariate time series is introduced that modifies the PSF algorithm to simultaneously forecast and backcast missing values for imputation. The imputePSF method extends PSF by characterizing repeating patterns of existing observations to provide a more precise estimate of missing values compared to more conventional methods, such as replacement with means or last observation carried forward. The imputation accuracy of imputePSF was evaluated by simulating varying amounts of missing observations with three univariate datasets. Comparisons of imputePSF with well-established methods using the same simulations demonstrated an overall reduction in error estimates. The imputePSF algorithm can produce more precise imputations on appropriate datasets, particularly those with periodic and repeating patterns.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energy2018,

title = {Stacking ensemble learning for short-term electricity consumption forecasting},

author = {F. Divina and A. Gilson and F. Goméz-Vela and M. García-Torres and J. F. Torres},

url = {https://www.mdpi.com/1996-1073/11/4/949},

doi = {https://doi.org/10.3390/en11040949},

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {4},

pages = {949},

abstract = {The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2017,

title = {Recent Advances in energy Time Series Forecasting},

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

url = {http://www.mdpi.com/1996-1073/10/6/809},

doi = {10.3390/en10060809},

year  = {2017},

date = {2017-01-01},

journal = {Energies},

volume = {10},

number = {6},

pages = {809},

abstract = {This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{CIN2017,

title = {Applications of computational intelligence in Time Series},

author = {F. Martínez-Álvarez and A. Troncoso and J. Reyes and M. Martínez-Ballesteros and J. C. Riquelme},

url = {https://www.hindawi.com/journals/cin/si/467684/},

doi = {10.1155/2017/9361749},

year  = {2017},

date = {2017-01-01},

journal = {Computational Intelligence and Neuroscience},

volume = {article id 9361749},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Bokde2016a,

title = {PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm},

author = {N. Bokde and G. Asencio-Cortes and F. Martinez-Alvarez and K. Kulat},

issn = {2073-4859},

year  = {2017},

date = {2017-01-01},

journal = {R Journal},

volume = {1},

number = {9},

pages = {324-333},

abstract = {This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its implementation with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example of usage. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{AsencioCortes2017,

title = {Temporal analysis of croatian seismogenic zones to improve earthquake magnitude prediction},

author = {G. Asencio-Cortes and S. Scitovski and R. Scitovski and F. Martinez-Alvarez},

doi = {10.1007/s12145-017-0295-5},

issn = {1865-0481},

year  = {2017},

date = {2017-01-01},

journal = {Earth Science Informatics},

volume = {3},

number = {10},

pages = {303-320},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Amaro-Mellado2017,

title = {Comparing seismic parameters for different source zone models in the Iberian Peninsula},

author = {J. L. Amaro-Mellado and A. Morales-Esteban and G. Asencio-Cortes and F. Martinez-Alvarez},

doi = {10.1016/j.tecto.2017.08.032},

issn = {0040-1951},

year  = {2017},

date = {2017-01-01},

journal = {Tectonophysics},

number = {717},

pages = {449-472},

abstract = {Seismical parameters of five seismogenic zonings for the Iberian Peninsula have been determined in this work. For that purpose, this research has two key goals. The first is to generate a seismic catalog. The second to calculate the seismical parameters of all the zones of the seismogenic zonings selected. The first key goal has been the creation of a catalog of earthquakes for the Iberian Peninsula and adjacent areas. First, the National Geographic Institute of Spain's catalog has been completed and reviewed with the information from other catalog reviews and specific studies. Second, all magnitude calculations have been homogenized. Third, all dependent data have been eliminated through declustering. Finally, the year of completeness for each magnitude has been considered. The Quaternary active faults database of Iberia has also been used as input data. All of this information has been integrated into a geographic information system. The second key aim is the calculation of the seismical parameters. The first parameter obtained has been the b-value. A method which considers different years of completeness in accordance with the magnitude has been used. Also, the annual rate of earthquakes per square kilometer has been calculated. Moreover, the maximum magnitude known that Quaternary active faults might generate and maximum magnitude recorded in the catalog have been determined. Finally, based solely on the statistical parameters obtained, a critical discussion of the seismogenic zonings of the Iberian Peninsula has been conducted. The results show that some zonings possess insufficient data for a proper calculation of the seismic parameters, from a statistical point of view.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{ASENCIO16,

title = {A novel methodology to predict urban traffic congestion with ensemble learning},

author = {G. Asencio-Cortés and E. Florido and A. Troncoso and F. Martínez-Álvarez},

url = {https://link.springer.com/article/10.1007/s00500-016-2288-6},

doi = {https://doi.org/10.1007/s00500-016-2288-6},

year  = {2016},

date = {2016-11-01},

journal = {Knowledge and Information Systems},

volume = {20},

pages = {4205–4216},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2016,

title = {A new methodology based on imbalanced classification for predicting outliers in electricity demand time series},

author = {F. J. Duque-Pintor and M. J. Fernández-Gómez and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.mdpi.com/1996-1073/9/9/752},

doi = {10.3390/en9090752},

year  = {2016},

date = {2016-01-01},

journal = {Energies},

volume = {9},

number = {9},

pages = {752},

abstract = {The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{SOFTCO2016,

title = {A novel methodology to predict urban traffic congestion with ensemble learning},

author = {G. Asencio-Cortés and E. Florido and A. Troncoso and F. Martínez-Álvarez},

url = {https://link.springer.com/article/10.1007/s00500-016-2288-6},

doi = {10.1007/s00500-016-2288-6},

year  = {2016},

date = {2016-01-01},

journal = {Soft Computing},

volume = {20},

number = {11},

pages = {4205-4216},

abstract = {Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Bokde2016,

title = {R package imputeTestbench to compare imputations methods for univariate time series},

author = {N. Bokde and K. Kulat and M. Beck W and G. Asencio-Cortes},

issn = {2073-4859},

year  = {2016},

date = {2016-01-01},

journal = {R Journal},

abstract = {This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are included with the package, including historical means, linear interpolation, and last observation carried forward. The testbench is not limited to the default functions and users can add or remove additional methods using a simple two-step process. The testbench compares the actual missing and imputed data for each method with different error metrics, including RMSE, MAE, and MAPE. Alternative error metrics can also be supplied by the user. The simplicity of use and significant reduction in time to compare imputation methods for missing data in univariate time series is a significant advantage of the package. This paper provides an overview of the core functions, including a demonstration with examples.},

keywords = {time series},

pubstate = {published},

tppubtype = {article}

}

@article{Asencio-Cortes2016,

title = {A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction},

author = {G. Asencio-Cortes and F. Martinez-Alvarez and A. Morales-Esteban and J. Reyes},

doi = {10.1016/j.knosys.2016.02.014},

issn = {0950-7051},

year  = {2016},

date = {2016-01-01},

journal = {Knowledge-Based Systems},

number = {101},

pages = {15-30},

abstract = {The use of different seismicity indicators as input for systems to predict earthquakes is becoming increasingly popular. Nevertheless, the values of these indicators have not been systematically obtained so far. This is mainly due to the gap of knowledge existing between seismologists and data mining experts. In this work, the effect of using different parameterizations for inputs in supervised learning algorithms has been thoroughly analyzed by means of a new methodology. Five different analyses have been conducted, mainly related to the shape of training and test sets, to the calculation of the b-value, and to the adjustment of most collected indicators. Outputs sensitivity has been determined when any of these factors is not properly taken into consideration. The methodology has been applied to four Chilean zones. Given its general-purpose design, it can be extended to any location. Similar conclusions have been drawn for all the cases: a proper selection of the sets length and a careful parameterization of certain indicators leads to significantly better results, in terms of prediction accuracy.},

keywords = {natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}