Citations and socials
Prof. Gualberto Asencio Cortés, Ph.D. is a Computer Science Engineer (University of Seville, 2008), Master in Software Engineering and Technology (University of Seville, 2010), Ph.D. (University of Pablo de Olavide, 2013) and has an Executive Master in Innovation (EOI, Spain, 2016). He is Associate Professor of Computer Science, in the area of Languages and Information Systems at the University of Pablo de Olavide. He is the author of 21 publications in impact journals according to JCR (18 of them between Q1 and Q2) and author of more than 22 articles in international and national conferences, most of them published in LNCS and LNBI. He has participated in three projects of the National Plan and three more of the Andalusian Research Plan. He is an editor of PLOS ONE (IF: 2.806, Q1), a regular reviewer of journals indexed in JCR (PLOS ONE, Bioinformatics, Neurocomputing, Computer and Geosciences, etc.) and member of the program committee in numerous international conferences. He has participated in more than 7 technology transfer contracts between the university and the company, including ISOTROL, Red Eléctrica Española and DETEA. He has 5 months of international research stays and 3 national months.
The research lines of Prof. Gualberto Asencio Cortés, Ph.D. are focused on data mining, machine learning, prediction of time series and bioinformatics, with different fields of application: prediction of natural series (seismic, air quality, meteorological, agronomic, …), prediction of electricity consumption and market prices, prediction of urban traffic, as well as bioinformatics in prediction of biological structures. He has also been data scientist and member of the steering committee responsible for artificial intelligence and data science technologies at the private company easytosee AgTech SL for more than 2 years (2015-2017).
Publications
2021 |
M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting Conference SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications, 1401 , Advances in Intelligent Systems and Computing 2021. @conference{JIMENEZ-NAVARRO21, title = {HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting}, author = {M. J. Jiménez-Navarro and F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés}, doi = {https://doi.org/10.1007/978-3-030-87869-6_68}, year = {2021}, date = {2021-09-01}, booktitle = {SOCO International Conference on Soft Computing Models in Industrial and Environmental Applications}, volume = {1401}, pages = {717-727}, series = {Advances in Intelligent Systems and Computing}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery Conference HAIS, 12886 , Lecture Notes in Computer Science 2021. @conference{MOLINA21, title = {A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery}, author = {M. A. Molina and M. J. Jiménez-Navarro and F. Martínez-Álvarez and G. Asencio-Cortés}, url = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_43}, doi = {https://doi.org/10.1007/978-3-030-86271-8_43}, year = {2021}, date = {2021-09-01}, booktitle = {HAIS}, volume = {12886}, pages = {511-523}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
2020 |
P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series Conference HAIS 15th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2020. @conference{HAIS2020, title = {A New Forecasting Algorithm Based on Neighbors for Streaming Electricity Time Series}, author = {P. Jiménez-Herrera and L. Melgar-García and G. Asencio-Cortés and A. Troncoso}, url = {https://link.springer.com/chapter/10.1007/978-3-030-61705-9_43}, year = {2020}, date = {2020-11-04}, booktitle = {HAIS 15th International Conference on Hybrid Artificial Intelligence Systems}, pages = {522-533}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model Journal Article Big Data, 8 (4), pp. 308-322, 2020. @article{MARTINEZ-ALVAREZ20, title = {Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model}, author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme}, url = {https://www.liebertpub.com/doi/full/10.1089/big.2020.0051}, doi = {10.1089/big.2020.0051}, year = {2020}, date = {2020-07-22}, journal = {Big Data}, volume = {8}, number = {4}, pages = {308-322}, abstract = {This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance. |
R. Pérez-Chacón and G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand Journal Article Information Sciences, 540 , pp. 160-174, 2020. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
2019 |
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 A novel ensemble method for electric vehicle power consumption forecasting: application to the Spanish system Journal Article IEEE Access, 7 , pp. 120840-120856, 2019. @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 = {}, pubstate = {published}, tppubtype = {article} } |
F. Martinez-Alvarez and A. Schmutz and G. Asencio-Cortes and J. Jacques A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand Journal Article Energies, 12 (94), pp. 1-18, 2019, ISSN: 1996-1073. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
2018 |
C. Rubio-Escudero and G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme Impact of Auto-evaluation Tests as Part of the Continuous Evaluation in Programming Courses Conference ICEUTE 9th International Conference on European Transnational Education, 771 , Advances in Intelligent Systems and Computing 2018. @conference{RUBIO18, title = {Impact of Auto-evaluation Tests as Part of the Continuous Evaluation in Programming Courses}, author = {C. Rubio-Escudero and G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme}, url = {https://link.springer.com/chapter/10.1007/978-3-319-94120-2_54}, doi = {https://doi.org/chapter/10.1007/978-3-319-94120-2_54}, year = {2018}, date = {2018-06-07}, booktitle = {ICEUTE 9th International Conference on European Transnational Education}, volume = {771}, pages = {553-561}, series = {Advances in Intelligent Systems and Computing}, abstract = {The continuous evaluation allows for the assessment of the progressive assimilation of concepts and the competences that must be achieved in a course. There are several ways to implement such continuous evaluation system. We propose auto-evaluation tests as a valuable tool for the student to judge his level of knowledge. Furthermore, these tests are also used as a small part of the continuous evaluation process, encouraging students to learn the concepts seen in the course, as they have the feeling that the time dedicated to this study will have an assured reward, binge able to answer correctly the questions in the continuous evaluation exams. New technologies are a great aid to improve the auto-evaluation experience both for the students and the teachers. In this research work we have compared the results obtained in courses where auto-evaluation tests were provided against courses where they were not provided, showing how the tests improve a set of quality metrics in the results of the course.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The continuous evaluation allows for the assessment of the progressive assimilation of concepts and the competences that must be achieved in a course. There are several ways to implement such continuous evaluation system. We propose auto-evaluation tests as a valuable tool for the student to judge his level of knowledge. Furthermore, these tests are also used as a small part of the continuous evaluation process, encouraging students to learn the concepts seen in the course, as they have the feeling that the time dedicated to this study will have an assured reward, binge able to answer correctly the questions in the continuous evaluation exams. New technologies are a great aid to improve the auto-evaluation experience both for the students and the teachers. In this research work we have compared the results obtained in courses where auto-evaluation tests were provided against courses where they were not provided, showing how the tests improve a set of quality metrics in the results of the course. |
A. Troncoso and P. Ribera and G. Asencio-Cortés and I. Vega and D. Gallego Imbalanced classification techniques for monsoon forecasting based on a new climatic time series Journal Article Environmental Modelling & Software, 106 (6), pp. 48-56, 2018. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
A. Gomez-Losada and G. Asencio-Cortes and F. Martinez-Alvarez and J. C. Riquelme A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information Journal Article Environmental Modelling and Software, (110), pp. 52-61, 2018, ISSN: 1364-8152. @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 = {}, pubstate = {published}, tppubtype = {article} } |
E. Florido and G. Asencio-Cortes and J. L. Aznarte and C. Rubio-Escudero and F. Martinez-Alvarez A novel tree-based algorithm to discover seismic patterns in earthquake catalogs Journal Article Computers and Geosciences, (115), pp. 96-104, 2018, ISSN: 0098-3004. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
G. Asencio-Cortes and A. Morales-Esteban and X. Shang and F. Martinez-Alvarez Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure Journal Article Computers and Geosciences, (115), pp. 198-210, 2018, ISSN: 0098-3004. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
X. Shang and X. Li and A. Morales-Esteban and G. Asencio-Cortes and Z. Wang Data field-based K-means clustering for spatio-temporal seismicity analysis and hazard assessment Journal Article Remote Sensing, 10 (461), pp. 1-22, 2018, ISSN: 2072-4292. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |
2017 |
N. Bokde and A. Troncoso and G. Asencio-Cortés and K. Kulat and F. Martínez-Álvarez Pattern sequence similarity based techniques for wind speed forecasting Conference ITISE International Work-Conference on Time Series Analysis, 2017. @conference{ITISE2017, title = {Pattern sequence similarity based techniques for wind speed forecasting}, author = {N. Bokde and A. Troncoso and G. Asencio-Cortés and K. Kulat and F. Martínez-Álvarez}, year = {2017}, date = {2017-01-01}, booktitle = {ITISE International Work-Conference on Time Series Analysis}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
N. Bokde and G. Asencio-Cortes and F. Martinez-Alvarez and K. Kulat PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm Journal Article R Journal, 1 (9), pp. 324-333, 2017, ISSN: 2073-4859. @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 = {}, pubstate = {published}, tppubtype = {article} } 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. |